qkpca-class: Class "qkpca"

In qkerntool: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools

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
[e-mail]

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))

qkerntool documentation built on May 2, 2019, 6:11 a.m.