kcca.rd

这是核学习的一个基础软件包

\name{kcca}
\alias{kcca}
\alias{kcca,matrix-method}
\title{Kernel Canonical Correlation Analysis}
\description{
Computes the canonical correlation analysis in a feature space.
}
\usage{
\S4method{kcca}{matrix}(x, y, kernel="rbfdot", kpar=list(sigma=0.1), ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{x}{a matrix containing data index by row}
  \item{y}{a matrix containing data index by row}
  \item{kernel}{the kernel function used in training and predicting.
    This parameter can be set to any function, of class kernel,
    which computes a dot product between two
    vector arguments. kernlab provides the most popular kernel functions
    which can be used by setting the kernel parameter to the following
    strings:
    \itemize{
      \item \code{rbfdot} (Radial Basis kernel function)
      \item \code{polydot} (Polynomial kernel function)
      \item \code{vanilladot} (Linear kernel function)
      \item \code{tanhdot} (Hyperbolic tangent kernel function)
    }
    The kernel parameter can also be set to a user defined function of
    class kernel by passing the function name as an argument.
  }
\item{kpar}{the list of hyper-parameters (kernel parameters).
    This is a list which contains the parameters to be used with the
    kernel function. For valid parameters for existing kernels are :
    \itemize{
      \item \code{sigma} (inverse kernel width for the Radial Basis kernel function "rbfdot")
      \item \code{degree, scale, offset} (for the Polynomial kernel "polydot")
      \item \code{scale, offset} (for the Hyperbolic tangent kernel
      function "tanhdot")
    }
    Hyper-parameters for user defined kernels can be passed through the
    kpar parameter as well.}

  \item{\dots}{adittional parameters for the \code{kpca} function}
}
\details{
The kernel version of the classical canonical correlation analysis. 
}
\value{
  An S4 object containg the following slots:
  \item{kcor}{Correlation coefficients in feature space}
  \item{xcoef}{estimated coefficients for the \code{x} variables in the
    feature space}
  \item{ycoef}{estimated coefficients for the \code{y} variables in the
    feature space}
  \item{xvar}{The canonical variates for \code{x}}
  \item{yvar}{The canonical variates for \code{y}}
    
}
\references{ ~put references to the literature/web site here ~ }
\author{ Alexandros Karatzoglou \cr \email{[e-mail]}}


\seealso{\code{cancor}  \code{kpca}}
\examples{

}
\keyword{multivariate}