Linear dimensionality reduction using relevance weighted LDA

The linear discriminant analysis (LDA) is one of the most traditional linear dimensionality reduction methods. This paper incorporates the inter-class relationships as relevance weights into the estimation of the over all within-class scatter matrix in order to improve the performance of the basic LDA method and some of its improved variants. We demonstrate that in son-re specific situations the standard multi-class LDA almost totally fails to find a discriminative sub-space if the proposed relevance weights are not incorporated. In order to estimate the relevance weights of individual within-class scatter matrices. we propose several methods of which one employs the evolution strategies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.