A Discriminative and Heteroscedastic Linear Feature Transformation for Multiclass Classification

This paper presents a novel discriminative feature transformation, named full-rank generalized likelihood ratio discriminant analysis (fGLRDA), on the grounds of the likelihood ratio test (LRT). fGLRDA attempts to seek a feature space, which is linearly isomorphic to the original n-dimensional feature space and is characterized by a full-rank )( nn× transformation matrix, under the assumption that all the classdiscrimination information resides in a d-dimensional subspace )( nd < , through making the most confusing situation, described by the null hypothesis, as unlikely as possible to happen without the homoscedastic assumption on class distributions. Our experimental results demonstrate that fGLRDA can yield moderate performance improvements over other existing methods, such as linear discriminant analysis (LDA) for the speaker identification task.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Hung-Shin Lee, Hsin-Min Wang, Berlin Chen
• Keywords: Computer Vision, Discriminant Analysis, Discriminative Feature Transformation, ICPR 2010, Likelihood Ratio