ICLinearDiscriminantClassifier: Implicitly Constrained Semi-supervised Linear Discriminant...

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

Implicitly Constrained Semi-supervised Linear Discriminant Classifier

Description

Semi-supervised version of Linear Discriminant Analysis using implicit constraints as described in (Krijthe & Loog 2014). This method finds the soft labeling of the unlabeled objects, whose resulting LDA solution gives the highest log-likelihood when evaluated on the labeled objects only. See also ICLeastSquaresClassifier.

Usage

ICLinearDiscriminantClassifier(X, y, X_u, prior = NULL, scale = FALSE,
  init = NULL, sup_prior = FALSE, x_center = FALSE, ...)

Arguments

X	design matrix of the labeled objects
y	vector with labels
X_u	design matrix of the labeled objects
prior	set a fixed class prior
scale	logical; Should the features be normalized? (default: FALSE)
init	not currently used
sup_prior	logical; use the prior estimates based only on the labeled data, not the imputed labels (default: FALSE)
x_center	logical; Whether the data should be centered
...	Additional Parameters, Not used

References

Krijthe, J.H. & Loog, M., 2014. Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. In International Conference on Pattern Recognition. Stockholm, pp. 3762-3767.

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, LinearDiscriminantClassifier, LinearSVM, LinearTSVM(), LogisticLossClassifier, LogisticRegression, MCLinearDiscriminantClassifier, MCNearestMeanClassifier, MCPLDA, MajorityClassClassifier, NearestMeanClassifier, QuadraticDiscriminantClassifier, S4VM, SVM, SelfLearning, TSVM, USMLeastSquaresClassifier, WellSVM, svmlin()

RSSL documentation built on May 29, 2024, 2:38 a.m.