LinearDiscriminantClassifier: Linear Discriminant Classifier

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

Linear Discriminant Classifier

Description

Implementation of the linear discriminant classifier. Classes are modeled as Gaussians with different means but equal covariance matrices. The optimal covariance matrix and means for the classes are found using maximum likelihood, which, in this case, has a closed form solution.

Usage

LinearDiscriminantClassifier(X, y, method = "closedform", prior = NULL,
  scale = FALSE, x_center = FALSE)

Arguments

X	Design matrix, intercept term is added within the function
y	Vector or factor with class assignments
method	the method to use. Either "closedform" for the fast closed form solution or "ml" for explicit maximum likelihood maximization
prior	A matrix with class prior probabilities. If NULL, this will be estimated from the data
scale	logical; If TRUE, apply a z-transform to the design matrix X before running the regression
x_center	logical; Whether the feature vectors should be centered

Value

S4 object of class LeastSquaresClassifier with the following slots:

modelform	weight vector
prior	the prior probabilities of the classes
mean	the estimates means of the classes
sigma	The estimated covariance matrix
classnames	a vector with the classnames for each of the classes
scaling	scaling object used to transform new observations

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, ICLinearDiscriminantClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, 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.