kpca-class: Class "kpca"

In kernlab: Kernel-Based Machine Learning Lab

Class "kpca"

Description

The Kernel Principal Components Analysis class

Objects of class "kpca"

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

Slots

pcv:

Object of class "matrix" containing the principal component vectors

eig:

Object of class "vector" containing the corresponding eigenvalues

rotated:

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

kernelf:

Object of class "function" containing the kernel function used

kpar:

Object of class "list" containing the kernel parameters used

xmatrix:

Object of class "matrix" containing the data matrix used

kcall:

Object of class "ANY" containing the function call

n.action:

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

Methods

eig

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

kcall

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

kernelf

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

pcv

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

predict

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

rotated

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

xmatrix

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

Author(s)

Alexandros Karatzoglou
[e-mail]

See Also

ksvm-class, kcca-class

Examples

# another example using the iris
data(iris)
test <- sample(1:50,20)

kpc <- kpca(~.,data=iris[-test,-5],kernel="rbfdot",
            kpar=list(sigma=0.2),features=2)

#print the principal component vectors
pcv(kpc)
rotated(kpc)
kernelf(kpc)
eig(kpc)

kernlab documentation built on Sept. 12, 2024, 6:51 a.m.