Direct Orthogonal Discriminant Analysis

Abstract

Orthogonal discriminant analysis algorithms have recently been proposed. However, these methods donpsilat address the singularity problem in the high dimensional feature space. In this paper, we present a new method called direct orthogonal discriminant analysis (DODA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space and does not suffer the singularity problem. This method is very simple and easy to be implemented. Experimental results show that the proposed method is very competitive in comparison with some existing dimensionality reduction algorithms.