EMLinearDiscriminantClassifier: Semi-Supervised Linear Discriminant Analysis using...

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

Semi-Supervised Linear Discriminant Analysis using Expectation Maximization

Description

Expectation Maximization applied to the linear discriminant classifier assuming Gaussian classes with a shared covariance matrix.

Usage

EMLinearDiscriminantClassifier(X, y, X_u, method = "EM", scale = FALSE,
  eps = 1e-08, verbose = FALSE, max_iter = 100)

Arguments

X	matrix; Design matrix for labeled data
y	factor or integer vector; Label vector
X_u	matrix; Design matrix for unlabeled data
method	character; Currently only "EM"
scale	logical; Should the features be normalized? (default: FALSE)
eps	Stopping criterion for the maximinimization
verbose	logical; Controls the verbosity of the output
max_iter	integer; Maximum number of iterations

Details

Starting from the supervised solution, uses the Expectation Maximization algorithm (see Dempster et al. (1977)) to iteratively update the means and shared covariance of the classes (Maximization step) and updates the responsibilities for the unlabeled objects (Expectation step).

References

Dempster, A., Laird, N. & Rubin, D., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39(1), pp.1-38.

See Also

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

jkrijthe/RSSL documentation built on Jan. 13, 2024, 1:56 a.m.