Package 'qkerntool'

Title: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools
Description: Nonlinear machine learning tool for classification, clustering and dimensionality reduction. It integrates 12 q-kernel functions and 15 conditional negative definite kernel functions and includes the q-kernel and conditional negative definite kernel version of density-based spatial clustering of applications with noise, spectral clustering, generalized discriminant analysis, principal component analysis, multidimensional scaling, locally linear embedding, sammon's mapping and t-Distributed stochastic neighbor embedding.
Authors: Yusen Zhang [aut, cre] , Daolin Pang [ctb], Jinghao Wang [ctb], Jialin Zhang [ctb]
Maintainer: Yusen Zhang <[email protected]>
License: GPL (>= 2)
Version: 1.19
Built: 2024-11-20 05:20:08 UTC
Source: https://github.com/cran/qkerntool

Help Index


Assing cndkernmatrix class to matrix objects

Description

as.cndkernmatrix in package qkerntool can be used to create the cndkernmatrix class to matrix objects representing a CND kernel matrix. These matrices can then be used with the cndkernmatrix interfaces which most of the functions in qkerntool support.

Usage

## S4 method for signature 'matrix'
as.cndkernmatrix(x, center = FALSE)

Arguments

x

matrix to be assigned the cndkernmatrix class

center

center the cndkernel matrix in feature space (default: FALSE)

Author(s)

Yusen Zhang
[email protected]

See Also

cndkernmatrix,qkernmatrix

Examples

## Create the data
x <- rbind(matrix(rnorm(10),,2),matrix(rnorm(10,mean=3),,2))
y <- matrix(c(rep(1,5),rep(-1,5)))

### Use as.cndkernmatrix to label the cov. matrix as a CND kernel matrix
### which is eq. to using a linear kernel

K <- as.cndkernmatrix(crossprod(t(x)))

K

Assing qkernmatrix class to matrix objects

Description

as.qkernmatrix in package qkerntool can be used to create the qkernmatrix class to matrix objects representing a q kernel matrix. These matrices can then be used with the qkernmatrix interfaces which most of the functions in qkerntool support.

Usage

## S4 method for signature 'matrix'
as.qkernmatrix(x, center = FALSE)

Arguments

x

matrix to be assigned the qkernmatrix class

center

center the kernel matrix in feature space (default: FALSE)

Author(s)

Yusen Zhang
[email protected]

See Also

qkernmatrix,cndkernmatrix

Examples

## Create the data
x <- rbind(matrix(rnorm(10),,2),matrix(rnorm(10,mean=3),,2))
y <- matrix(c(rep(1,5),rep(-1,5)))

### Use as.qkernmatrix to label the cov. matrix as a qkernel matrix
### which is eq. to using a linear kernel

K <- as.qkernmatrix(crossprod(t(x)))

K

qKernel Functions

Description

The kernel generating functions provided in qkerntool.
The Non Linear Kernel k(x,y)=12(1q)(qαx2+qαy22qαxy)k(x,y) = \frac{1}{2(1-q)}(q^{-\alpha||x||^2}+q^{-\alpha||y||^2}-2q^{-\alpha x'y}).
The Gaussian kernel k(x,y)=11q(1q(xy2/σ))k(x,y) =\frac{1}{1-q} (1-q^{(||x-y||^2/\sigma)}).
The Laplacian Kernel k(x,y)=11q(1q(xy/σ))k(x,y) =\frac{1}{1-q} (1-q^{(||x-y||/\sigma)}).

The Rational Quadratic Kernel k(x,y)=11q(1qxy2xy2+c)k(x,y) =\frac{1}{1-q} (1-q^{\frac{||x-y||^2}{||x-y||^2+c}}).
The Multiquadric Kernel k(x,y)=11q(qcqxy2+c)k(x,y) =\frac{1}{1-q} (q^c-q^{\sqrt{||x-y||^2+c}}).
The Inverse Multiquadric Kernel k(x,y)=11q(q1cq1xy2+c)k(x,y) =\frac{1}{1-q} (q^{-\frac{1}{c}}-q^{-\frac{1}{\sqrt{||x-y||^2+c}}}).
The Wave Kernel k(x,y)=11q(q1qθxysinxyθ)k(x,y) =\frac{1}{1-q} (q^{-1}-q^{-\frac{\theta}{||x-y||}\sin{\frac{||x-y||}{\theta}}}).
The d Kernel k(x,y)=11q[1q(xyd)]k(x,y) = \frac{1}{1-q}[1-q^(||x-y||^d)].
The Log Kernel k(x,y)=11q[1qln(xyd+1)]k(x,y) =\frac{1}{1-q} [1-q^ln(||x-y||^d+1)].
The Cauchy Kernel k(x,y)=11q(q1q11+xy2/σ)k(x,y) =\frac{1}{1-q} (q^{-1}-q^{-\frac{1}{1+||x-y||^2/\sigma}}).
The Chi-Square Kernel k(x,y)=11q(1q2(xy)2/(x+y)γ)k(x,y) =\frac{1}{1-q} (1-q^{\sum{2(x-y)^2/(x+y)} \gamma}).
The Generalized T-Student Kernel k(x,y)=11q(q1q11+xyd)k(x,y) =\frac{1}{1-q} (q^{-1}-q^{-\frac{1}{1+||x-y||^d}}).

Usage

rbfbase(sigma=1,q=0.8)
nonlbase(alpha = 1,q = 0.8)
laplbase(sigma = 1, q = 0.8)
ratibase(c = 1, q = 0.8)
multbase(c = 1, q = 0.8)
invbase(c = 1, q = 0.8)
wavbase(theta = 1,q = 0.8)
powbase(d = 2, q = 0.8)
logbase(d = 2, q = 0.8)
caubase(sigma = 1, q = 0.8)
chibase(gamma = 1, q = 0.8)
studbase(d = 2, q = 0.8)

Arguments

q

for all the qkernel function.

sigma

for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

alpha

for the Non Linear qkernel function "nonlbase".

c

for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

theta

for the Wave qkernel function "wavbase".

d

for the d qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

gamma

for the Chi-Square qkernel function "chibase".

Details

The kernel generating functions are used to initialize a kernel function which calculates the kernel function value between two feature vectors in a Hilbert Space. These functions can be passed as a qkernel argument on almost all functions in qkerntool(e.g., qkgda, qkpca etc).

Value

Return an S4 object of class qkernel which extents the function class. The resulting function implements the given kernel calculating the kernel function value between two vectors.

qpar

a list containing the kernel parameters (hyperparameters) used.

The kernel parameters can be accessed by the qpar function.

Author(s)

Yusen Zhang
[email protected]

See Also

qkernmatrix, cndkernmatrix

Examples

qkfunc <- rbfbase(sigma=1,q=0.8)
qkfunc

qpar(qkfunc)

## create two vectors
x <- rnorm(10)
y <- rnorm(10)

## calculate dot product
qkfunc(x,y)

Block diagonal concatenation of matrix

Description

Y = BLKDIAG(A,B,...) produces diag(A,B,...)

Usage

blkdiag(x)

Arguments

x

a list of matrix

Value

E - Block diagonal concatenation of matrix

Author(s)

Yusen Zhang
[email protected]


Class "cndkernel" "nonlkernel" "polykernel" "rbfkernel" "laplkernel"

Description

The built-in kernel classes in qkerntool

Objects from the Class

Objects can be created by calls of the form new("nonlkernel"), new{"polykernel"}, new{"rbfkernel"}, new{"laplkernel"}, new{"anokernel"}, new{"ratikernel"}, new{"multkernel"}, new{"invkernel"}, new{"wavkernel"}, new{"powkernel"}, new{"logkernel"}, new{"caukernel"}, new{"chikernel"}, new{"studkernel"},new{"norkernel"}

or by calling the nonlcnd,polycnd, rbfcnd, laplcnd, anocnd, raticnd, multcnd, invcnd, wavcnd, powcnd, logcnd, caucnd, chicnd, studcnd, norcnd functions etc..

Slots

.Data:

Object of class "function" containing the kernel function

qpar:

Object of class "list" containing the kernel parameters

Methods

cndkernmatrix

signature(kernel = "rbfkernel", x ="matrix"): computes the kernel matrix

Author(s)

Yusen Zhang
[email protected]

See Also

qkernmatrix,cndkernmatrix

Examples

cndkfunc <- rbfcnd(gamma = 1)
cndkfunc

qpar(cndkfunc)

## create two vectors
x <- rnorm(10)
y <- rnorm(10)


cndkfunc(x,y)

CND Kernel Matrix functions

Description

cndkernmatrix calculates the kernel matrix Kij=k(xi,xj)K_{ij} = k(x_i,x_j) or Kij=k(xi,yj)K_{ij} = k(x_i,y_j).

Usage

## S4 method for signature 'cndkernel'
cndkernmatrix(cndkernel, x, y = NULL)

Arguments

cndkernel

the cndkernel function to be used to calculate the CND kernel matrix. This has to be a function of class cndkernel, i.e. which can be generated either one of the build in kernel generating functions (e.g., rbfcnd nonlcnd etc.) or a user defined function of class cndkernel taking two vector arguments and returning a scalar.

x

a data matrix to be used to calculate the kernel matrix.

y

second data matrix to calculate the kernel matrix.

Details

Common functions used during kernel based computations.
The cndkernel parameter can be set to any function, of class cndkernel, which computes the kernel function value in feature space between two vector arguments. qkerntool provides more than 10 CND kernel functions which can be initialized by using the following functions:

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Gaussian cndkernel function

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd d cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

(see example.)

Value

cndkernmatrix returns a conditionally negative definite matrix with a zero diagonal element.

Author(s)

Yusen Zhang
[email protected]

See Also

nonlbase, rbfbase, laplbase, ratibase, multbase, invbase, wavbase, powbase, logbase, caubase, chibase, studbase

Examples

## use the iris data
data(iris)
dt <- as.matrix(iris[ ,-5])

## initialize cndkernel function
lapl <- laplcnd(gamma = 1)
lapl

## calculate cndkernel matrix
cndkernmatrix(lapl, dt)

CND Kernel Functions

Description

The kernel generating functions provided in qkerntool.
The Non Linear Kernel k(x,y)=[exp(αx2)+exp(αy2)2exp(αxy)]/2k(x,y) = [exp(\alpha ||x||^2)+exp(\alpha||y||^2)-2exp(\alpha x'y)]/2.
The Polynomial kernel k(x,y)=[(αx2+c)d+(αy2+c)d2(αxy+c)d]/2k(x,y) = [(\alpha ||x||^2+c)^d+(\alpha ||y||^2+c)^d-2(\alpha x'y+c)^d]/2.
The Gaussian kernel k(x,y)=1exp(xy2/γ)k(x,y) = 1-exp(-||x-y||^2/\gamma).
The Laplacian Kernel k(x,y)=1exp(xy/γ)k(x,y) = 1-exp(-||x-y||/\gamma).
The ANOVA Kernel k(x,y)=nexp(σ(xy)2)dk(x,y) = n-\sum exp(-\sigma (x-y)^2)^d.
The Rational Quadratic Kernel k(x,y)=xy2/(xy2+c)k(x,y) = ||x-y||^2/(||x-y||^2+c).
The Multiquadric Kernel k(x,y)=(xy2+c2)ck(x,y) = \sqrt{(||x-y||^2+c^2)-c}.
The Inverse Multiquadric Kernel k(x,y)=1/c1/xy2+c2k(x,y) = 1/c-1/\sqrt{||x-y||^2+c^2}.
The Wave Kernel k(x,y)=1θxysinxyθk(x,y) = 1-\frac{\theta}{||x-y||}\sin\frac{||x-y||}{\theta}.
The d Kernel k(x,y)=xydk(x,y) = ||x-y||^d.
The Log Kernel k(x,y)=log(xyd+1)k(x,y) = \log(||x-y||^d+1).
The Cauchy Kernel k(x,y)=11/(1+xy2/γ)k(x,y) = 1-1/(1+||x-y||^2/\gamma).
The Chi-Square Kernel k(x,y)=2(xy)2/(x+y)k(x,y) = \sum{2(x-y)^2/(x+y)}.
The Generalized T-Student Kernel k(x,y)=11/(1+xyd)k(x,y) = 1-1/(1+||x-y||^d).
The normal Kernel k(x,y)=xy2k(x,y) = ||x-y||^2.

Usage

nonlcnd(alpha = 1)
polycnd(d = 2, alpha = 1, c = 1)
rbfcnd(gamma = 1)
laplcnd(gamma = 1)
anocnd(d = 2, sigma = 1)
raticnd(c = 1)
multcnd(c = 1)
invcnd(c = 1)
wavcnd(theta = 1)
powcnd(d = 2)
logcnd(d = 2)
caucnd(gamma = 1)
chicnd( )
studcnd(d = 2)
norcnd()

Arguments

alpha

for the Non Linear cndkernel function "nonlcnd" and the Polynomial cndkernel function "polycnd".

gamma

for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

sigma

for the ANOVA cndkernel function "anocnd".

theta

for the Wave cndkernel function "wavcnd".

c

for the Rational Quadratic cndkernel function "raticnd", the Polynomial cndkernel function "polycnd", the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

d

for the Polynomial cndkernel function "polycnd", the ANOVA cndkernel function "anocnd", the cndkernel function "powcnd", the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Details

The kernel generating functions are used to initialize a kernel function which calculates the kernel function value between two feature vectors in a Hilbert Space. These functions can be passed as a qkernel argument on almost all functions in qkerntool.

Value

Return an S4 object of class cndkernel which extents the function class. The resulting function implements the given kernel calculating the kernel function value between two vectors.

qpar

a list containing the kernel parameters (hyperparameters) used.

The kernel parameters can be accessed by the qpar function.

Author(s)

Yusen Zhang
[email protected]

See Also

cndkernmatrix, qkernmatrix

Examples

cndkfunc <- rbfcnd(gamma = 1)
cndkfunc

qpar(cndkfunc)

## create two vectors
x <- rnorm(10)
y <- rnorm(10)

## calculate dot product
cndkfunc(x,y)

Computes the Euclidean(square Euclidean) distance matrix

Description

Eucdist Computes the Euclidean(square Euclidean) distance matrix.

Arguments

x

(NxD) matrix (N samples, D features)

y

(MxD) matrix (M samples, D features)

sEuclidean

can be TRUE or FALSE, FALSE to Compute the Euclidean distance matrix.

Value

E - (MxN) Euclidean (square Euclidean) distances between vectors in x and y

Author(s)

Yusen Zhang
[email protected]

Examples

###
data(iris)
testset <- sample(1:150,20)
x <- as.matrix(iris[-testset,-5])
y <- as.matrix(iris[testset,-5])

##
res0 <- Eucdist(x)
res1 <- Eucdist(x, x, sEuclidean = FALSE)
res2 <- Eucdist(x, y = NULL, sEuclidean = FALSE)
res3 <- Eucdist(x, x, sEuclidean = TRUE)
res4 <- Eucdist(x, y = NULL)
res5 <- Eucdist(x, sEuclidean = FALSE)

mfeat_pix dataset

Description

This dataset consists of features of handwritten numerals (‘0’–‘9’) extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images. This dataset is about 240 pixel averages in 2 x 3 windows

Usage

data("mfeat_pix")

Format

A data frame with 2000 observations on the following 240 variables.

Source

https://archive.ics.uci.edu/ml/datasets/Multiple+Features

Examples

data(mfeat_pix)

qKernel-DBSCAN density reachability and connectivity clustering

Description

Similiar to the Density-Based Spatial Clustering of Applications with Noise(or DBSCAN) algorithm, qKernel-DBSCAN is a density-based clustering algorithm that can be applied under both linear and non-linear situations.

Usage

## S4 method for signature 'matrix'
qkdbscan(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
eps = 0.25, MinPts = 5, hybrid = TRUE, seeds = TRUE,  showplot  = FALSE,
countmode = NULL, na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qkdbscan(x, eps = 0.25, MinPts = 5, seeds = TRUE,
showplot  = FALSE, countmode = NULL, ...)

## S4 method for signature 'qkernmatrix'
qkdbscan(x, eps = 0.25, MinPts = 5, seeds = TRUE,
showplot  = FALSE, countmode = NULL, ...)

## S4 method for signature 'qkdbscan'
predict(object, data, newdata = NULL, predict.max = 1000, ...)

Arguments

x

the data matrix indexed by row, or a kernel matrix of cndkernmatrix or qkernmatrix.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • power, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • power, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • power for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

eps

reachability distance, see Ester et al. (1996). (default:0.25)

MinPts

reachability minimum number of points, see Ester et al.(1996).(default : 5)

hybrid

whether the algothrim expects raw data but calculates partial distance matrices, can be TRUE or FALSE

seeds

can be TRUE or FALSE, FALSE to not include the isseed-vector in the dbscan-object.

showplot

whether to show the plot or not, can be TRUE or FALSE

na.action

a function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

countmode

NULL or vector of point numbers at which to report progress.

object

object of class dbscan.

data

matrix or data.frame.

newdata

matrix or data.frame with raw data to predict.

predict.max

max. batch size for predictions.

...

Further arguments transferred to plot methods.

Details

The data can be passed to the qkdbscan function in a matrix, in addition qkdbscan also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix.

Value

predict(qkdbscan-method) gives out a vector of predicted clusters for the points in newdata.

qkdbscan gives out an S4 object which is a LIST with components

clust

integer vector coding cluster membership with noise observations (singletons) coded as 0

eps

parameter eps

MinPts

parameter MinPts

kcall

the function call

cndkernf

the kernel function used

xmatrix

the original data matrix

all the slots of the object can be accessed by accessor functions.

Note

The predict function can be used to embed new data on the new space.

Author(s)

Yusen Zhang
[email protected]

References

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu(1996).
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Institute for Computer Science, University of Munich.
Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96)

See Also

qkernmatrix, cndkernmatrix

Examples

# a simple example using the iris
data(iris)
test <- sample(1:150,20)
x<- as.matrix(iris[-test,-5])
ds <- qkdbscan (x,kernel="laplbase",qpar=list(sigma=3.5,q=0.8),eps=0.15,
MinPts=5,hybrid = FALSE)
plot(ds,x)
emb <- predict(ds, x, as.matrix(iris[test,-5]))
points(iris[test,], col= as.integer(1+emb))

Class "qkdbscan"

Description

The qkernel-DBSCAN class.

Objects of class "qkdbscan"

Objects can be created by calls of the form new("qkdbscan", ...). or by calling the qkdbscan function.

Slots

clust:

Object of class "vector" containing the cluster membership of the samples

eps:

Object of class "numeric" containing the reachability distance

MinPts:

Object of class "numeric" containing the reachability minimum number of points

isseed:

Object of class "logical" containing the logical vector indicating whether a point is a seed (not border, not noise)

Methods

clust

signature(object = "qkdbscan"): returns the cluster membership

kcall

signature(object = "qkdbscan"): returns the performed call

cndkernf

signature(object = "qkdbscan"): returns the used kernel function

eps

signature(object = "qkdbscan"): returns the reachability distance

MinPts

signature(object = "qkdbscan"): returns the reachability minimum number of points

predict

signature(object = "qkdbscan"): embeds new data

xmatrix

signature(object = "qkdbscan"): returns the used data matrix

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class

Examples

# a simple example using the iris data
x<- as.matrix(iris[,-5])
ds <- qkdbscan (x,kernel="laplbase",qpar=list(sigma=3.5,q=0.8),eps=0.15,
MinPts=5,hybrid = FALSE)
# print the results
clust(ds)
eps(ds)
MinPts(ds)
cndkernf(ds)
xmatrix(ds)
kcall(ds)

Class "qkernel" "rbfqkernel" "nonlqkernel" "laplqkernel" "ratiqkernel"

Description

The built-in kernel classes in qkerntool

Objects from the Class

Objects can be created by calls of the form new("rbfqkernel"), new{"nonlqkernel"}, new{"laplqkernel"}, new{"ratiqkernel"}, new{"multqkernel"}, new{"invqkernel"}, new{"wavqkernel"}, new{"powqkernel"}, new{"logqkernel"}, new{"cauqkernel"}, new{"chiqkernel"}, new{"studqkernel"}

or by calling the rbfbase, nonlbase, laplbase, ratibase, multbase, invbase, wavbase, powbase, logbase, caubase, chibase, studbase functions etc..

Slots

.Data:

Object of class "function" containing the kernel function

qpar:

Object of class "list" containing the kernel parameters

Methods

qkernmatrix

signature(kernel = "rbfqkernel", x = "matrix"): computes the qkernel matrix

Author(s)

Yusen Zhang
[email protected]

See Also

qkernmatrix,cndkernmatrix

Examples

qkfunc <- rbfbase(sigma=1,q=0.8)
qkfunc

qpar(qkfunc)

## create two vectors
x <- rnorm(10)
y <- rnorm(10)

## calculate dot product
qkfunc(x,y)

qKernel Matrix functions

Description

qkernmatrix calculates the qkernel matrix Kij=k(xi,xj)K_{ij} = k(x_i,x_j) or Kij=k(xi,yj)K_{ij} = k(x_i,y_j).

Usage

## S4 method for signature 'qkernel'
qkernmatrix(qkernel, x, y = NULL)

Arguments

qkernel

the kernel function to be used to calculate the qkernel matrix. This has to be a function of class qkernel, i.e. which can be generated either one of the build in kernel generating functions (e.g., rbfbase etc.) or a user defined function of class qkernel taking two vector arguments and returning a scalar.

x

a data matrix to be used to calculate the kernel matrix

y

second data matrix to calculate the kernel matrix

Details

Common functions used during kernel based computations.
The qkernel parameter can be set to any function, of class qkernel, which computes the kernel function value in feature space between two vector arguments. qkerntool provides more than 10 qkernel functions which can be initialized by using the following functions:

  • nonlbase Non Linear qkernel function

  • rbfbase Gaussian qkernel function

  • laplbase Laplacian qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase d qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

(see example.)

Value

qkernmatrix returns a conditionally negative definite matrix with a zero diagonal element.

Author(s)

Yusen Zhang
[email protected]

See Also

nonlcnd, rbfcnd,polycnd,laplcnd, anocnd, raticnd, multcnd, invcnd, wavcnd, powcnd, logcnd, caucnd, chicnd, studcnd

Examples

data(iris)
dt <- as.matrix(iris[ ,-5])

## initialize kernel function
rbf <- rbfbase(sigma = 1.4, q=0.8)
rbf

## calculate qkernel matrix
qkernmatrix(rbf, dt)

qKernel Generalized Discriminant Analysis

Description

The qkernel Generalized Discriminant Analysis is a method that deals with nonlinear discriminant analysis using kernel function operator.

Usage

## S4 method for signature 'matrix'
qkgda(x, label, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
          features = 0, th = 1e-4, na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qkgda(x, label, features = 0, th = 1e-4, na.action = na.omit, ...)
## S4 method for signature 'qkernmatrix'
qkgda(x, label, features = 0, th = 1e-4, ...)

Arguments

x

the data matrix indexed by row, or a kernel matrix of cndkernmatrix or qkernmatrix.

label

The original labels of the samples.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

features

Number of features (principal components) to return. (default: 0 , all)

th

the value of the eigenvalue under which principal components are ignored (only valid when features = 0). (default : 0.0001)

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

The qkernel Generalized Discriminant Analysis method provides a mapping of the input vectors into high dimensional feature space, generalizing the classical Linear Discriminant Analysis to non-linear discriminant analysis.
The data can be passed to the qkgda function in a matrix, in addition qkgda also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix.

Value

An S4 object containing the eigenvectors and their normalized projections, along with the corresponding eigenvalues and the original function.

prj

The normalized projections on eigenvectors)

eVal

The corresponding eigenvalues

eVec

The corresponding eigenvectors

kcall

The formula of the function called

cndkernf

The kernel function used

xmatrix

The original data matrix

all the slots of the object can be accessed by accessor functions.

Note

The predict function can be used to embed new data on the new space

Author(s)

Yusen Zhang
[email protected]

References

1.Baudat, G, and F. Anouar:
Generalized discriminant analysis using a kernel approach
Neural Computation 12.10(2000),2385
2.Deng Cai, Xiaofei He, and Jiawei Han:
Speed Up Kernel Discriminant Analysis
The VLDB Journal,January,2011,vol.20, no.1,21-33.

See Also

qkernmatrix, cndkernmatrix

Examples

Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3)))
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
test <- as.matrix(iris[testset,-5])
Sp = rep(c("1","2","3"), rep(50,3))
labels <-as.numeric(Sp)
trainlabel <- labels[-testset]
testlabel <- labels[testset]

kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2)

prj(kgda1)
eVal(kgda1)
eVec(kgda1)
kcall(kgda1)
# xmatrix(kgda1)

#print the principal component vectors
prj(kgda1)
#plot the data projection on the components
plot(kgda1@prj,col=as.integer(train), xlab="1st Principal Component",ylab="2nd Principal Component")

Class "qkgda"

Description

The qkernel Generalized Discriminant Analysis class

Objects of class "qkgda"

Objects can be created by calls of the form new("qkgda", ...). or by calling the qkgda function.

Slots

prj:

Object of class "matrix" containing the normalized projections on eigenvectors

eVal:

Object of class "matrix" containing the corresponding eigenvalues

eVec:

Object of class "matrix" containing the corresponding eigenvectors

label:

Object of class "matrix" containing the categorical variables that the categorical data be assigned to one of the categories

Methods

prj

signature(object = "qkgda"): returns the normalized projections

eVal

signature(object = "qkgda"): returns the eigenvalues

eVec

signature(object = "qkgda"): returns the eigenvectors

kcall

signature(object = "qkgda"): returns the performed call

cndkernf

signature(object = "qkgda"): returns the used kernel function

predict

signature(object = "qkgda"): embeds new data

xmatrix

signature(object = "qkgda"): returns the used data matrix

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class

Examples

Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3)))
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
test <- as.matrix(iris[testset,-5])
Sp = rep(c("1","2","3"), rep(50,3))
labels <-as.numeric(Sp)
trainlabel <- labels[-testset]
testlabel <- labels[testset]

kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2)

prj(kgda1)
eVal(kgda1)
eVec(kgda1)
cndkernf(kgda1)
kcall(kgda1)

qKernel Isometric Feature Mapping

Description

Computes the Isomap embedding as introduced in 2000 by Tenenbaum, de Silva and Langford.

Usage

## S4 method for signature 'matrix'
qkIsomap(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
dims = 2, k, mod = FALSE, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qkIsomap(x, dims = 2, k, mod = FALSE, plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)

## S4 method for signature 'qkernmatrix'
qkIsomap(x, dims = 2, k, mod = FALSE, plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)

Arguments

x

N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

dims

vector containing the target space dimension(s)

k

number of neighbours

mod

use modified Isomap algorithm

plotResiduals

show a plot with the residuals between the high and the low dimensional data

verbose

show a summary of the embedding procedure at the end

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

The qkIsomap is a nonlinear dimension reduction technique, that preserves global properties of the data. That means, that geodesic distances between all samples are captured best in the low dimensional embedding.
This R version is based on the Matlab implementation by Tenenbaum and uses Floyd's Algorithm to compute the neighbourhood graph of shortest distances, when calculating the geodesic distances.
A modified version of the original Isomap algorithm is included. It respects nearest and farthest neighbours.
To estimate the intrinsic dimension of the data, the function can plot the residuals between the high and the low dimensional data for a given range of dimensions.

Value

qkIsomap gives out an S4 object which is a LIST with components

prj

a N x dim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension was specified).

dims

the dimension of the target space.

Residuals

the residual variances for all dimensions.

eVal

the corresponding eigenvalues.

eVec

the corresponding eigenvectors.

cndkernf

the kernel function used.

kcall

The formula of the function called

all the slots of the object can be accessed by accessor functions.

Author(s)

Yusen Zhang
[email protected]

References

Tenenbaum, J. B. and de Silva, V. and Langford, J. C., "A global geometric framework for nonlinear dimensionality reduction.", 2000; Matlab code is available at http://waldron.stanford.edu/~isomap/

Examples

# another example using the iris
  data(iris)
  testset <- sample(1:150,20)
  train <- as.matrix(iris[-testset,-5])
  labeltrain<- as.integer(iris[-testset,5])
  test <- as.matrix(iris[testset,-5])
  # ratibase(c=1,q=0.8)
  d_low = qkIsomap(train, kernel = "ratibase", qpar = list(c=1,q=0.8),
                    dims=2,  k=5, plotResiduals = TRUE)
  #plot the data projection on the components
  plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd  Principal Component")

  prj(d_low)
	dims(d_low)
	Residuals(d_low)
	eVal(d_low)
	eVec(d_low)
	kcall(d_low)
	cndkernf(d_low)

qKernel Isomap embedding

Description

The qKernel Isometric Feature Mapping class

Objects of class "qkIsomap"

Objects can be created by calls of the form new("qkIsomap", ...). or by calling the qkIsomap function.

Slots

prj:

Object of class "matrix" containing the Nxdim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension specified)

dims:

Object of class "numeric" containing the dimension of the target space (default 2)

connum:

Object of class "numeric" containing the number of connected components in graph

Residuals:

Object of class "vector" containing the residual variances for all dimensions

eVal:

Object of class "vector" containing the corresponding eigenvalues

eVec:

Object of class "vector" containing the corresponding eigenvectors

Methods

prj

signature(object = "qkIsomap"): returns the Nxdim matrix (N samples, dim features)

dims

signature(object = "qkIsomap"): returns the dimension

Residuals

signature(object = "qkIsomap"): returns the residual variances

eVal

signature(object = "qkIsomap"): returns the eigenvalues

eVec

signature(object = "qkIsomap"): returns the eigenvectors

xmatrix

signature(object = "qkIsomap"): returns the used data matrix

kcall

signature(object = "qkIsomap"): returns the performed call

cndkernf

signature(object = "qkIsomapa"): returns the used kernel function

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class, qkIsomap

Examples

# another example using the iris data
  data(iris)
  testset <- sample(1:150,20)
  train <- as.matrix(iris[-testset,-5])
  labeltrain<- as.integer(iris[-testset,5])
  test <- as.matrix(iris[testset,-5])
  # ratibase(c=1,q=0.8)
  d_low = qkIsomap(train, kernel = "ratibase", qpar = list(c=1,q=0.8),
                    dims=2,  k=5, plotResiduals = TRUE)
  #plot the data projection on the components
  plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd  Principal Component")

  prj(d_low)
	dims(d_low)
	Residuals(d_low)
	eVal(d_low)
	eVec(d_low)
	kcall(d_low)
	cndkernf(d_low)

qKernel Locally Linear Embedding

Description

Computes the qkernel Locally Linear Embedding

Usage

## S4 method for signature 'matrix'
qkLLE(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
                         dims = 2, k, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qkLLE(x, dims = 2, k, na.action = na.omit, ...)
## S4 method for signature 'qkernmatrix'
qkLLE(x, dims = 2, k, na.action = na.omit,...)

Arguments

x

N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • power, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • power, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • power for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

dims

dimension of the target space

k

the number of nearest neighbours.

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

The qkernel Locally Linear Embedding (qkLLE) preserves local properties of the data by representing each sample in the data by a linear combination of its k nearest neighbours with each neighbour weighted independently. qkLLE finally chooses the low-dimensional representation that best preserves the weights in the target space. It is an extension of Locally Linear Embedding (LLE) with qkernel method.

Value

It returns an S4 object containing the principal component vectors along with the corresponding eigenvalues.

prj

a matrix with the reduced input data

dims

dimension of the target space

eVal

The corresponding eigenvalues

eVec

The corresponding eigenvectors

cndkernf

the kernel function used

all the slots of the object can be accessed by accessor functions.

Author(s)

Yusen Zhang
[email protected]

References

Roweis, Sam T. and Saul, Lawrence K., "Nonlinear Dimensionality Reduction by Locally Linear Embedding",2000;

Examples

## S4 method for signature 'matrix'
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
labeltrain<- as.integer(iris[-testset,5])
test <- as.matrix(iris[testset,-5])
plot(train ,col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component")
# ratibase(c=1,q=0.8)
d_low <- qkLLE(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5)
#plot the data projection on the components
plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component")

## S4 method for signature 'qkernmatrix'
# ratibase(c=0.1,q=0.8)
qkfunc <- ratibase(c=0.1,q=0.8)
ktrain1 <- qkernmatrix(qkfunc,train)
d_low <- qkLLE(ktrain1, dims = 2, k=5)
#plot the data projection on the components
plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component")

Class "qkLLE"

Description

The qKernel Locally Linear Embedding class

Objects of class "qkLLE"

Objects can be created by calls of the form new("qkLLE", ...). or by calling the qkLLE function.

Slots

prj:

Object of class "matrix" containing the reduced input data

dims:

Object of class "numeric" containing the dimension of the target space (default 2)

eVal:

Object of class "vector" containing the corresponding eigenvalues

eVec:

Object of class "matrix" containing the corresponding eigenvectors

Methods

prj

signature(object = "qkLLE"): returns the reduced input data

dims

signature(object = "qkLLE"): returns the dimension

eVal

signature(object = "qkLLE"): returns the eigenvalues

eVec

signature(object = "qkLLE"): returns the eigenvectors

xmatrix

signature(object = "qkLLE"): returns the used data matrix

kcall

signature(object = "qkLLE"): returns the performed call

cndkernf

signature(object = "qkLLE"): returns the used kernel function

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class

Examples

## S4 method for signature 'matrix'
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
labeltrain<- as.integer(iris[-testset,5])
test <- as.matrix(iris[testset,-5])
plot(train ,col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component")
# ratibase(c=1,q=0.8)
d_low <- qkLLE(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5)
#plot the data projection on the components
plot(prj(d_low),col=labeltrain,xlab="1st Principal Component",ylab="2nd Principal Component")

## S4 method for signature 'qkernmatrix'
# ratibase(c=0.1,q=0.8)
qkfunc <- ratibase(c=0.1,q=0.8)
ktrain1 <- qkernmatrix(qkfunc,train)
d_low <- qkLLE(ktrain1, dims = 2, k=5)
#plot the data projection on the components
plot(prj(d_low),col=labeltrain,xlab="1st Principal Component",ylab="2nd Principal Component")

qKernel Metric Multi-Dimensional Scaling

Description

The qkernel Metric Multi-Dimensional Scaling is a nonlinear form of Metric Multi-Dimensional Scaling

Usage

## S4 method for signature 'matrix'
qkMDS(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
dims = 2, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qkMDS(x, dims = 2,plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)

## S4 method for signature 'qkernmatrix'
qkMDS(x, dims = 2,plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)

Arguments

x

N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

dims

vector containing the target space dimension(s)

plotResiduals

show a plot with the residuals between the high and the low dimensional data

verbose

show a summary of the embedding procedure at the end

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

There are several versions of non-metric multidimensional scaling in R, but qkerntool offers the following unique combination of using qKernel methods

Value

qkMDS gives out an S4 object which is a LIST with components

prj

a N x dim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension was specified).

dims

the dimension of the target space.

Residuals

the residual variances for all dimensions.

eVal

the corresponding eigenvalues.

eVec

the corresponding eigenvectors.

cndkernf

the kernel function used.

kcall

The formula of the function called

all the slots of the object can be accessed by accessor functions.

Author(s)

Yusen Zhang
[email protected]

References

Kruskal, J.B. 1964a. Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1–28.

Examples

# another example using the iris
  data(iris)
  testset <- sample(1:150,20)
  train <- as.matrix(iris[-testset,-5])
  labeltrain<- as.integer(iris[-testset,5])
  test <- as.matrix(iris[testset,-5])
  # ratibase(c=1,q=0.8)
  d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.9),dims = 2,
                 plotResiduals = TRUE)
  #plot the data projection on the components
  plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd  Principal Component")

  prj(d_low)
	dims(d_low)
	Residuals(d_low)
	eVal(d_low)
	eVec(d_low)
	kcall(d_low)
	cndkernf(d_low)

qKernel Metric Multi-Dimensional Scaling

Description

The qkernel Metric Multi-Dimensional Scaling class

Objects of class "qkMDS"

Objects can be created by calls of the form new("qkMDS", ...). or by calling the qkMDS function.

Slots

prj:

Object of class "matrix" containing the Nxdim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension specified)

dims:

Object of class "numeric" containing the dimension of the target space (default 2)

connum:

Object of class "numeric" containing the number of connected components in graph

Residuals:

Object of class "vector" containing the residual variances for all dimensions

eVal:

Object of class "vector" containing the corresponding eigenvalues

eVec:

Object of class "vector" containing the corresponding eigenvectors

Methods

prj

signature(object = "qkMDS"): returns the Nxdim matrix (N samples, dim features)

dims

signature(object = "qkMDS"): returns the dimension

Residuals

signature(object = "qkMDS"): returns the residual variances

eVal

signature(object = "qkMDS"): returns the eigenvalues

eVec

signature(object = "qkMDS"): returns the eigenvectors

xmatrix

signature(object = "qkMDS"): returns the used data matrix

kcall

signature(object = "qkMDS"): returns the performed call

cndkernf

signature(object = "qkMDS"): returns the used kernel function

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class, qkMDS

Examples

# another example using the iris
  data(iris)
  testset <- sample(1:150,20)
  train <- as.matrix(iris[-testset,-5])
  labeltrain<- as.integer(iris[-testset,5])
  test <- as.matrix(iris[testset,-5])
  # ratibase(c=1,q=0.8)
  d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.8),
                    dims=2, plotResiduals = TRUE)
  #plot the data projection on the components
  plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd  Principal Component")

  prj(d_low)
	dims(d_low)
	Residuals(d_low)
	eVal(d_low)
	eVec(d_low)
	kcall(d_low)
	cndkernf(d_low)

qKernel Principal Components Analysis

Description

The qkernel Principal Components Analysis is a nonlinear form of principal component analysis.

Usage

## S4 method for signature 'formula'
qkpca(x, data = NULL, na.action, ...)
## S4 method for signature 'matrix'
qkpca(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
                        features = 0, th = 1e-4, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qkpca(x, features = 0, th = 1e-4, ...)
## S4 method for signature 'qkernmatrix'
qkpca(x, features = 0, th = 1e-4, ...)

Arguments

x

the data matrix indexed by row, a formula describing the model or a kernel matrix of cndkernmatrix or qkernmatrix.

data

an optional data frame containing the variables in the model (when using a formula).

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase d qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the d qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

features

Number of features (principal components) to return. (default: 0 , all)

th

the value of the eigenvalue under which principal components are ignored (only valid when features = 0). (default : 0.0001)

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

Using kernel functions one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some non-linear map.
The data can be passed to the qkpca function in a matrix, in addition qkpca also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix.

Value

An S4 object containing the principal component vectors along with the corresponding eigenvalues.

pcv

a matrix containing the principal component vectors (column wise)

eVal

The corresponding eigenvalues

rotated

The original data projected (rotated) on the principal components

cndkernf

the kernel function used

xmatrix

The original data matrix

all the slots of the object can be accessed by accessor functions.

Note

The predict function can be used to embed new data on the new space

Author(s)

Yusen Zhang
[email protected]

References

Schoelkopf B., A. Smola, K.-R. Mueller :
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation 10, 1299-1319
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.1366

See Also

qkernmatrix, cndkernmatrix

Examples

# another example using the iris data
data(iris)
test <- sample(1:150,20)
qkpc <- qkpca(~.,data=iris[-test,-5],kernel="rbfbase",
              qpar=list(sigma=50,q=0.8),features=2)

# print the principal component vectors
pcv(qkpc)
#plot the data projection on the components
plot(rotated(qkpc),col=as.integer(iris[-test,5]),
     xlab="1st Principal Component",ylab="2nd Principal Component")

# embed remaining points
emb <- predict(qkpc,iris[test,-5])
points(emb,col=as.integer(iris[test,5]))

Class "qkpca"

Description

The qkernel Principal Components Analysis class

Objects of class "qkpca"

Objects can be created by calls of the form new("qkpca", ...). or by calling the qkpca function.

Slots

pcv:

Object of class "matrix" containing the principal component vectors

eVal:

Object of class "vector" containing the corresponding eigenvalues

rotated:

Object of class "matrix" containing the projection of the data on the principal components

Methods

eVal

signature(object = "qkpca"): returns the eigenvalues

pcv

signature(object = "qkpca"): returns the principal component vectors

predict

signature(object = "qkpca"): embeds new data

rotated

signature(object = "qkpca"): returns the projected data

xmatrix

signature(object = "qkpca"): returns the used data matrix

kcall

signature(object = "qkpca"): returns the performed call

cndkernf

signature(object = "qkpca"): returns the used kernel function

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class

Examples

# another example using the iris data
  data(iris)
  test <- sample(1:150,20)
  qkpc <- qkpca(~.,iris[-test,-5], kernel = "rbfbase",
                qpar = list(sigma = 50, q = 0.8), features = 2)

  # print the principal component vectors
  pcv(qkpc)
  rotated(qkpc)
  cndkernf(qkpc)
  eVal(qkpc)
  xmatrix(qkpc)
  names(eVal(qkpc))

Class "qkprc"

Description

The qKernel Prehead class

Objects of class "qkprc"

Objects from the class cannot be created directly but only contained in other classes.

Slots

cndkernf:

Object of class "kfunction" containing the kernel function used

qpar:

Object of class "list" containing the kernel parameters used

xmatrix:

Object of class "input" containing the data matrix used

ymatrix:

Object of class "input" containing the data matrix used

kcall:

Object of class "ANY" containing the function call

terms:

Object of class "ANY" containing the function terms

n.action:

Object of class "ANY" containing the action performed on NA

Methods

cndkernf

signature(object = "qkprc"): returns the used kernel function

xmatrix

signature(object = "qkprc"): returns the used data matrix

ymatrix

signature(object = "qkprc"): returns the used data matrix

kcall

signature(object = "qkprc"): returns the performed call

Author(s)

Yusen Zhang
[email protected]

See Also

qkernel-class, cndkernel-class


qkernel spectral Clustering

Description

A qkernel spectral clustering algorithm. Clustering is performed by embedding the data into the subspace of the eigenvectors of a graph Laplacian matrix.

Usage

## S4 method for signature 'matrix'
qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 2, q = 0.9),
          Nocent=NA, normalize="symmetric", maxk=20, iterations=200,
          na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qkspecc(x, Nocent=NA, normalize="symmetric",
          maxk=20,iterations=200, ...)

## S4 method for signature 'qkernmatrix'
qkspecc(x, Nocent=NA, normalize="symmetric",
          maxk=20,iterations=200, ...)

Arguments

x

the matrix of data to be clustered or a kernel Matrix of class qkernmatrix or cndkernmatrix.

kernel

the kernel function used in computing the affinity matrix. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase d qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd d cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

a character string or the list of hyper-parameters (kernel parameters). The default character string list(sigma = 2, q = 0.9) uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. The second option "local" (local scaling) uses a more advanced heuristic and sets a width parameter for every point in the data set. This is particularly useful when the data incorporates multiple scales. A list can also be used containing the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the d qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". where length is the length of the strings considered, lambda the decay factor and normalized a logical parameter determining if the kernel evaluations should be normalized.

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

Nocent

the number of clusters.

normalize

Normalisation of the Laplacian ("none", "symmetric" or "random-walk").

maxk

If k is NA, an upper bound for the automatic estimation. Defaults to 20.

iterations

the maximum number of iterations allowed.

na.action

the action to perform on NA.

...

additional parameters.

Details

The qkernel spectral clustering works by embedding the data points of the partitioning problem into the subspace of the eigenvectors corresponding to the kk smallest eigenvalues of the graph Laplacian matrix. Using a simple clustering method like kmeans on the embedded points usually leads to good performance. It can be shown that qkernel spectral clustering methods boil down to graph partitioning.
The data can be passed to the qkspecc function in a matrix, in addition qkspecc also supports input in the form of a kernel matrix of class qkernmatrix or cndkernmatrix.

Value

An S4 object of class qkspecc which extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information

clust

The cluster assignments

eVec

The corresponding eigenvector

eVal

The corresponding eigenvalues

ymatrix

The eigenvectors corresponding to the kk smallest eigenvalues of the graph Laplacian matrix.

Author(s)

Yusen Zhang
[email protected]

References

Andrew Y. Ng, Michael I. Jordan, Yair Weiss
On Spectral Clustering: Analysis and an Algorithm
Neural Information Processing Symposium 2001

See Also

qkernmatrix, cndkernmatrix, qkpca

Examples

data("iris")
x=as.matrix(iris[,-5])

qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 10, q = 0.9),
                Nocent=3, normalize="symmetric", maxk=15, iterations=1200)
plot(x, col = clust(qspe))

qkfunc <- nonlbase(alpha=1/15,q=0.8)
Ktrain <- qkernmatrix(qkfunc, x)
qspe <- qkspecc(Ktrain, Nocent=3, normalize="symmetric", maxk=20)
plot(x, col = clust(qspe))

Class "qkspecc"

Description

The qKernel Spectral Clustering Class

Objects from the Class

Objects can be created by calls of the form new("qkspecc", ...). or by calling the function qkspecc.

Slots

clust:

Object of class "vector" containing the cluster assignments

eVec:

Object of class "matrix" containing the corresponding eigenvector in each cluster

eVal:

Object of class "vector" containing the corresponding eigenvalue for each cluster

withinss:

Object of class "vector" containing the within-cluster sum of squares for each cluster

Methods

clust

signature(object = "qkspecc"): returns the cluster assignments

eVec

signature(object = "qkspecc"): returns the corresponding eigenvector in each cluster

eVal

signature(object = "qkspecc"): returns the corresponding eigenvalue for each cluster

xmatrix

signature(object = "qkspecc"): returns the original data matrix or a kernel Matrix

ymatrix

signature(object = "qkspecc"): returns The eigenvectors corresponding to the kk smallest eigenvalues of the graph Laplacian matrix.

cndkernf

signature(object = "qkspecc"): returns the used kernel function

kcall

signature(object = "qkspecc"): returns the performed call

Author(s)

Yusen Zhang
[email protected]

See Also

qkspecc, qkernel-class, cndkernel-class

Examples

## Cluster the iris data set.
data("iris")
x=as.matrix(iris[,-5])

qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 10, q = 0.9),
                Nocent=3, normalize="symmetric", maxk=15, iterations=1200)
clust(qspe)
eVec(qspe)
eVal(qspe)
xmatrix(qspe)
ymatrix(qspe)
cndkernf(qspe)

qkernel spectral Clustering

Description

This is also a qkernel spectral clustering algorithm which uses three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan.

Usage

## S4 method for signature 'qkspecc'
qkspeclust(x, clustmethod = "kmeans",
         Nocent=NULL,iterations=NULL, hmethod=NULL,eps = NULL, MinPts = NULL)

Arguments

x

object of class qkspecc.

clustmethod

the strategy to use to assign labels in the embedding space. There are three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan.

Nocent

the number of clusters

iterations

the maximum number of iterations allowed for "kmeans".

hmethod

the agglomeration method for "hclust". This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

eps

Reachability distance for "dbscan".

MinPts

Reachability minimum no. of points for "dbscan".

Details

The qkernel spectral clustering works by embedding the data points of the partitioning problem into the subspace of the eigenvectors corresponding to the kk smallest eigenvalues of the graph Laplacian matrix. Using the simple clustering methods like kmeans, hclust and dbscan on the embedded points usually leads to good performance. It can be shown that qkernel spectral clustering methods boil down to graph partitioning.

Value

An S4 object of class qkspecc which extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information

clust

The cluster assignments

eVec

The corresponding eigenvector

eVal

The corresponding eigenvalues

xmatrix

The original data matrix

ymatrix

The real valued matrix of eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix

cndkernf

The kernel function used

Author(s)

Yusen Zhang
[email protected]

References

Andrew Y. Ng, Michael I. Jordan, Yair Weiss
On Spectral Clustering: Analysis and an Algorithm
Neural Information Processing Symposium 2001

See Also

qkernmatrix, cndkernmatrix, qkspecc-class, qkspecc

Examples

data("iris")
x=as.matrix(iris[ ,-5])

qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 90, q = 0.9),
                Nocent=3, normalize="symmetric", maxk=15,iterations=1200)
plot(x, col = clust(qspe))

qspec <- qkspeclust(qspe,clustmethod = "hclust", Nocent=3, hmethod="ward.D2")
plot(x, col = clust(qspec))
plot(qspec)

qKernel Sammon Mapping

Description

The qkernel Sammon Mapping is an implementation for Sammon mapping, one of the earliest dimension reduction techniques that aims to find low-dimensional embedding that preserves pairwise distance structure in high-dimensional data space. qsammon is a nonlinear form of Sammon Mapping.

Usage

## S4 method for signature 'matrix'
qsammon(x, kernel = "rbfbase", qpar = list(sigma = 0.5, q = 0.9),
          dims = 2, Initialisation = 'random', MaxHalves = 20,
          MaxIter = 500, TolFun = 1e-7, na.action = na.omit, ...)

## S4 method for signature 'cndkernmatrix'
qsammon(cndkernel, x, k, dims = 2, Initialisation = 'random',
          MaxHalves = 20,MaxIter = 500, TolFun = 1e-7, ...)

## S4 method for signature 'qkernmatrix'
qsammon(qkernel, x, k, dims = 2, Initialisation = 'random',
          MaxHalves = 20, MaxIter = 500, TolFun = 1e-7, ...)

Arguments

x

the data matrix indexed by row or a kernel matrix of cndkernmatrix or qkernmatrix.

kernel

the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase d qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd d cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase".

  • alpha, q for the Non Linear qkernel function "nonlbase".

  • c, q for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase".

  • theta, q for the Wave qkernel function "wavbase".

  • d, q for the d qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase".

  • alpha for the Non Linear cndkernel function "nonlcnd".

  • d, alpha, c for the Polynomial cndkernel function "polycnd".

  • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • d, sigma for the ANOVA cndkernel function "anocnd".

  • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • theta for the Wave cndkernel function "wavcnd".

  • d for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

Hyper-parameters for user defined kernels can be passed through the qpar parameter as well.

qkernel

the kernel function to be used to calculate the qkernel matrix.

cndkernel

the cndkernel function to be used to calculate the CND kernel matrix.

k

the dimension of the original data.

dims

Number of features to return. (default: 2)

Initialisation

"random" or "pca"; the former performs fast random projection and the latter performs standard PCA (default : "random")

MaxHalves

maximum number of step halvings. (default : 20)

MaxIter

the maximum number of iterations allowed. (default : 500)

TolFun

relative tolerance on objective function. (default : 1e-7)

na.action

A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.)

...

additional parameters

Details

Using kernel functions one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some non-linear map.
The data can be passed to the qsammon function in a matrix, in addition qsammon also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix.

Value

dimRed

The matrix whose rows are embedded observations.

kcall

The function call contained

cndkernf

The kernel function used

all the slots of the object can be accessed by accessor functions.

Author(s)

Yusen Zhang
[email protected]

References

Sammon, J.W. (1969) A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers, C-18 5:401-409.

See Also

qkernmatrix, cndkernmatrix

Examples

data(iris)
train <- as.matrix(iris[,1:4])
labeltrain<- as.integer(iris[,5])
## S4 method for signature 'matrix'
kpc2 <- qsammon(train, kernel = "rbfbase", qpar = list(sigma = 2, q = 0.9), dims = 2,
                Initialisation = 'pca', TolFun = 1e-5)
plot(dimRed(kpc2), col = as.integer(labeltrain))
cndkernf(kpc2)

Class "qsammon"

Description

The qKernel Sammon Mapping class

Objects of class "qsammon"

Objects can be created by calls of the form new("qsammon", ...). or by calling the qsammon function.

Slots

dimRed:

Object of class "matrix" containing the matrix whose rows are embedded observations

cndkernf:

Object of class "function" containing the kernel function used

kcall:

Object of class "ANY" containing the function call

Methods

dimRed

signature(object = "qsammon"): returns the matrix whose rows are embedded observations

kcall

signature(object = "qsammon"): returns the performed call

cndkernf

signature(object = "qsammon"): returns the used kernel function

Author(s)

Yusen Zhang
[email protected]

See Also

qsammon

Examples

data(iris)
  train <- as.matrix(iris[,1:4])
  labeltrain<- as.integer(iris[,5])
  ## S4 method for signature 'matrix'
  qkpc <- qsammon(train, kernel = "rbfbase", qpar = list(sigma = 0.5, q = 0.9),
                   dims = 2, Initialisation = 'pca', MaxHalves = 50)

  cndkernf(qkpc)
  dimRed(qkpc)
  kcall(qkpc)

qKernel t-Distributed Stochastic Neighbor Embedding

Description

Wrapper for the qkernel t-distributed stochastic neighbor embeddingg. qtSNE is a method for constructing a low dimensional embedding of high-dimensional data, distances or similarities.

Usage

## S4 method for signature 'matrix'
qtSNE(x,kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
        initial_config = NULL, no_dims=2, initial_dims=30, perplexity=30, max_iter= 1300,
         min_cost=0, epoch_callback=NULL, epoch=100, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30,
        perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100)
## S4 method for signature 'qkernmatrix'
qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30,
        perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100)

Arguments

x

the matrix of data to be clustered or a kernel Matrix of class qkernmatrix or cndkernmatrix.

kernel

the kernel function used in computing the affinity matrix. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:

  • rbfbase Radial Basis qkernel function "Gaussian"

  • nonlbase Non Linear qkernel function

  • laplbase Laplbase qkernel function

  • ratibase Rational Quadratic qkernel function

  • multbase Multiquadric qkernel function

  • invbase Inverse Multiquadric qkernel function

  • wavbase Wave qkernel function

  • powbase Power qkernel function

  • logbase Log qkernel function

  • caubase Cauchy qkernel function

  • chibase Chi-Square qkernel function

  • studbase Generalized T-Student qkernel function

  • nonlcnd Non Linear cndkernel function

  • polycnd Polynomial cndkernel function

  • rbfcnd Radial Basis cndkernel function "Gaussian"

  • laplcnd Laplacian cndkernel function

  • anocnd ANOVA cndkernel function

  • raticnd Rational Quadratic cndkernel function

  • multcnd Multiquadric cndkernel function

  • invcnd Inverse Multiquadric cndkernel function

  • wavcnd Wave cndkernel function

  • powcnd Power cndkernel function

  • logcnd Log cndkernel function

  • caucnd Cauchy cndkernel function

  • chicnd Chi-Square cndkernel function

  • studcnd Generalized T-Student cndkernel function

The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument.

qpar

a character string or the list of hyper-parameters (kernel parameters). The default character string list(sigma = 2, q = 0.9) uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. The second option "local" (local scaling) uses a more advanced heuristic and sets a width parameter for every point in the data set. This is particularly useful when the data incorporates multiple scales. A list can also be used containing the parameters to be used with the kernel function. Valid parameters for existing kernels are :

  • sigma for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" the Cauchy qkernel function "caubase" and for the ANOVA cndkernel function "anocnd".

  • alpha for the Non Linear qkernel function "nonlbase",for the Non Linear cndkernel function "nonlcnd",and for the Polynomial cndkernel function "polycnd".

  • c for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase", the Inverse Multiquadric qkernel function "invbase",for the Polynomial cndkernel function "polycnd",for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd".

  • d for qkernel function "powbase" , the Log qkernel function "logbase", the Generalized T-Student qkernel function "studbase", for the Polynomial cndkernel function "polycnd", for the ANOVA cndkernel function "anocnd",for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd".

  • theta for the Wave qkernel function "wavbase" and for the Wave cndkernel function "wavcnd".

  • gamma for the Chi-Square qkernel function "chibase",for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd".

  • q For all qkernel Function. where length is the length of the strings considered, lambda the decay factor and normalized a logical parameter determining if the kernel evaluations should be normalized.

Hyper-parameters for user defined kernels can be passed through the qkpar parameter as well.

initial_config

An intitial configure about x (default: NULL)

no_dims

the dimension of the resulting embedding. (default: 2)

initial_dims

The number of dimensions to use in reduction method. (default: 30)

perplexity

Perplexity parameter

max_iter

Number of iterations (default: 1300)

min_cost

The minimum cost for every object after the final iteration

epoch_callback

A callback function used after each epoch (an epoch here means a set number of iterations)

epoch

The interval of the number of iterations displayed (default: 100)

na.action

the action to perform on NA

...

Other arguments that can be passed to qtSNE

Details

When the initial_config argument is specified, the algorithm will automatically enter the final momentum stage. This stage has less large scale adjustment to the embedding, and is intended for small scale tweaking of positioning. This can greatly speed up the generation of embeddings for various similar X datasets, while also preserving overall embedding orientation.

Value

qtSNE gives out an S4 object which is a LIST with components

dimRed

Matrix containing the new representations for the objects after qtSNE

cndkernf

The kernel function used

Author(s)

Yusen Zhang
[email protected]

References

Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245.

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

Examples

## Not run: 
#use iris data set
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[,1:4])

colors = rainbow(length(unique(iris$Species)))
names(colors) = unique(iris$Species)
#for matrix
ecb = function(x,y){
  plot(x,t='n');
  text(x,labels=iris$Species, col=colors[iris$Species])
}
kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8),
              epoch_callback = ecb, perplexity=10, max_iter = 500)


## End(Not run)

Class "qtSNE"

Description

An S4 Class for qtSNE.

Details

The qtSNE is a method that uses Qkernel t-Distributed Stochastic Neighborhood Embedding between the distance matrices in high and low-dimensional space to embed the data. The method is very well suited to visualize complex structures in low dimensions.

Objects from the Class

Objects can be created by calls of the form new("qtSNE", ...). or by calling the function qtSNE.

Slots

dimRed

Matrix containing the new representations for the objects after qtSNE

cndkernf

The kernel function used

Method

dimRed

signature(object="qtSNE"): return a new representation matrix

cndkernf

signature(object="qtSNE"): return the kernel used

Author(s)

Yusen Zhang
[email protected]

References

Maaten, L. van der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15, 3221-3245.

van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605.

See Also

qtSNE

Examples

## Not run: 
#use iris data set
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[,1:4])

colors = rainbow(length(unique(iris$Species)))
names(colors) = unique(iris$Species)
#for matrix
ecb = function(x,y){
  plot(x,t='n');
  text(x,labels=iris$Species, col=colors[iris$Species])
}
kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8),
              epoch_callback = ecb, perplexity=10, max_iter = 500)

#cndernf
cndkernf(kpc2)

#dimRed
plot(dimRed(kpc2),col=train)


## End(Not run)