Abstract
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.
Original language | English |
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Pages (from-to) | 485-493 |
Number of pages | 9 |
Journal | Pattern Recognition |
Volume | 38 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Apr 2005 |
Keywords
- feature extraction
- weighted LDA
- Mahalanobis distance
- evolution strategies
- linear discriminant analysis
- Chernoff criterion
- approximate pairwise accuracy criterion