% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RSSRMR.R
\name{RSSRMR}
\alias{RSSRMR}
\title{Robust Self-Representation Sparse Reconstruction and Manifold Regularization}
\usage{
RSSRMR(x, Wt, L, alpha = 1, beta = 1, epsilon = 0.001, maxites = 50)
}
\arguments{
\item{x}{Numeric data matrix.}

\item{Wt}{Weight matrix.}

\item{L}{Laplacian matrix.}

\item{alpha}{Regularization parameter.}

\item{beta}{Graph regularization parameter.}

\item{epsilon}{Convergence threshold.}

\item{maxites}{Maximum number of iterations.}
}
\value{
A list containing:
\describe{
\item{Optimum.A}{Coefficient matrix}
\item{Optimum.G}{Diagonal weight matrix}
}
}
\description{
Performs robust sparse self-representation with manifold regularization.
}
\examples{
set.seed(6542)

cluster1 <- matrix(
  rnorm(12 * 5, mean = 2, sd = 0.5),
  nrow = 12
)

cluster2 <- matrix(
  rnorm(13 * 5, mean = 7, sd = 0.5),
  nrow = 13
)

X <- rbind(cluster1, cluster2)

wd <- diag(runif(25))
lp <- diag(runif(25))

fit <- RSSRMR(
  x = X,
  Wt = wd,
  L = lp
)

fit$Optimum.G

}
