Robust Reduced Rank Mixture Discriminant Analysis

ABSTRACT In the case of a large number of feature vector variables, using multivariate Gaussian mixture models, discrimination in a reduced subspace is studied, generalizing Hastie and Tibshirani's (1996) work, to a situation in which the outliers are present in the data. In the case of the Gaussian Mixture models, the reduced rank discriminant analysis is equivalent to the weighted rank k linear discriminant analysis (LDA). The reduced rank solution in the mixtures of multivariate Gaussian models was obtained from the full rank robust mixture solution. The classification in the new dimensions was compared with the discriminant analysis approach based on the original coordinates, using robust S-estimators. In most of the cases, the robust reduced rank mixture discriminant analysis (mda) performed better for the test data. However, for the case of common component covariance being diagonal, the robust reduced rank mixture discriminant analysis performed better than the robust full rank mixture discriminant analysis producing smaller errors in classification.