qkIsomap-class {qkerntool}

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
[e-mail]

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)