\name{kpca-class}\docType{class}\alias{kpca-class}\alias{eig}\alias{pcv}\alias{rotated}\alias{eig,kpca-method}\alias{kcall,kpca-method}\alias{kernelf,kpca-method}\alias{pcv,kpca-method}\alias{rotated,kpca-method}\alias{xmatrix,kpca-method}\title{Class "kpca"}\description{ The Kernel Principal Components Analysis class}\section{Objects objects of class "kpca"}{Objects can be created by calls of the form \code{new("kpca", ...)}.    or by calling the \code{kpca} function.}\section{Slots}{  \describe{    \item{\code{pcv}:}{Object of class \code{"matrix"} containing the      principal component vectors }    \item{\code{eig}:}{Object of class \code{"vector"} containing the      coresponding eigenvalues}    \item{\code{rotated}:}{Object of class \code{"matrix"} containing the      projection of the data on the principal components}    \item{\code{kernelf}:}{Object of class \code{"function"} containing      the kernel function used}    \item{\code{kpar}:}{Object of class \code{"list"} containing the      kernel parameters used }    \item{\code{xmatrix}:}{Object of class \code{"matrix"} conatining      the data matrix used }    \item{\code{kcall}:}{Object of class \code{"ANY"} containing the      function call }    \item{\code{n.action}:}{Object of class \code{"ANY"} containg the      action performed on NA }  }}\section{Methods}{  \describe{    \item{eig}{\code{signature(object = "kpca")}: returns the eigenvalues }    \item{kcall}{\code{signature(object = "kpca")}: returns the      performed call}    \item{kernelf}{\code{signature(object = "kpca")}: returns the used      kernel function}    \item{pcv}{\code{signature(object = "kpca")}: returns the principal      component vectors }    \item{predict}{\code{signature(object = "kpca")}: embeeds new data }    \item{rotated}{\code{signature(object = "kpca")}: returns the      projected data}    \item{xmatrix}{\code{signature(object = "kpca")}: returns the used      data matrix }  }}\author{Alexandros Karatzoglou\cr \email{alexandros.karatzoglou@ci.tuwien.ac.at}}\seealso{  \code{\link{ksvm-class}},    \code{\link{kcca-class}} }\examples{# another example using the irisdata(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 vectorspcv(kpc)rotated(kpc)kernelf(kpc)eig(kpc)}\keyword{classes}