Maximum Contrastive Pessimistic Likelihood Estimation for Linear Discriminant Analysis
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).
MCPLDA(X, y, X_u, x_center = FALSE, scale = FALSE, max_iter = 1000)
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
max_iter |
integer; Maximum number of iterations |
Loog, M., 2016. Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), pp.462-475.
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
ICLinearDiscriminantClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearSVM,
LinearTSVM(),
LogisticLossClassifier,
LogisticRegression,
MCLinearDiscriminantClassifier,
MCNearestMeanClassifier,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
WellSVM,
svmlin()
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