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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MCPLDA.R
\name{MCPLDA}
\alias{MCPLDA}
\title{Maximum Contrastive Pessimistic Likelihood Estimation for Linear Discriminant Analysis}
\usage{
MCPLDA(X, y, X_u, x_center = FALSE, scale = FALSE, max_iter = 1000)
}
\arguments{
\item{X}{matrix; Design matrix for labeled data}
\item{y}{factor or integer vector; Label vector}
\item{X_u}{matrix; Design matrix for unlabeled data}
\item{x_center}{logical; Should the features be centered?}
\item{scale}{logical; Should the features be normalized? (default: FALSE)}
\item{max_iter}{integer; Maximum number of iterations}
}
\description{
Maximum Contrastive Pessimistic Likelihood (MCPL) estimation (Loog 2016) attempts to find a semi-supervised solution that has a higher likelihood compared to the supervised solution on the labeled and unlabeled data even for the worst possible labeling of the data. This is done by attempting to find a saddle point of the maximin problem, where the max is over the parameters of the semi-supervised solution and the min is over the labeling, while the objective is the difference in likelihood between the semi-supervised and the supervised solution measured on the labeled and unlabeled data. The implementation is a translation of the Matlab code of Loog (2016).
}
\references{
Loog, M., 2016. Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), pp.462-475.
}
\seealso{
Other RSSL classifiers:
\code{\link{EMLeastSquaresClassifier}},
\code{\link{EMLinearDiscriminantClassifier}},
\code{\link{GRFClassifier}},
\code{\link{ICLeastSquaresClassifier}},
\code{\link{ICLinearDiscriminantClassifier}},
\code{\link{KernelLeastSquaresClassifier}},
\code{\link{LaplacianKernelLeastSquaresClassifier}()},
\code{\link{LaplacianSVM}},
\code{\link{LeastSquaresClassifier}},
\code{\link{LinearDiscriminantClassifier}},
\code{\link{LinearSVM}},
\code{\link{LinearTSVM}()},
\code{\link{LogisticLossClassifier}},
\code{\link{LogisticRegression}},
\code{\link{MCLinearDiscriminantClassifier}},
\code{\link{MCNearestMeanClassifier}},
\code{\link{MajorityClassClassifier}},
\code{\link{NearestMeanClassifier}},
\code{\link{QuadraticDiscriminantClassifier}},
\code{\link{S4VM}},
\code{\link{SVM}},
\code{\link{SelfLearning}},
\code{\link{TSVM}},
\code{\link{USMLeastSquaresClassifier}},
\code{\link{WellSVM}},
\code{\link{svmlin}()}
}
\concept{RSSL classifiers}