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\name{kpca-class}
\docType{class}
\alias{kpca-class}
\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 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
corresponding 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"} containing
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"} containing 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")}: embeds 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 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)
}
\keyword{classes}
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