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\name{svd.triplet}
\alias{svd.triplet}
\title{Singular Value Decomposition of a Matrix}
\description{
Compute the singular-value decomposition of a rectangular matrix with weights for rows and columns.
}
\usage{
svd.triplet(X, row.w=NULL, col.w=NULL, ncp=Inf)
}
\arguments{
\item{X}{a data matrix}
\item{row.w}{vector with the weights of each row (NULL by default and the weights are uniform)}
\item{col.w}{vector with the weights of each column (NULL by default and the weights are uniform)}
\item{ncp}{the number of components kept for the outputs}
}
\value{
\item{vs}{ a vector containing the singular values of 'x';}
\item{u}{a matrix whose columns contain the left singular vectors of 'x';}
\item{v}{a matrix whose columns contain the right singular vectors of 'x'.}
}
\seealso{ \code{\link{svd}}}
\keyword{algebra}
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