Linear dimensionality reduction using relevance weighted LDA

E.K. TANG, Ponnuthurai Nagaratnam SUGANTHAN, Xin YAO, A.K. QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

92 Citations (Scopus)

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 overall 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 some specific situations the standard multi-class LDA almost totally fails to find a discriminative subspace 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. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)485-493
Number of pages9
JournalPattern Recognition
Volume38
Issue number4
Early online date15 Dec 2004
DOIs
Publication statusPublished - Apr 2005
Externally publishedYes

Keywords

  • Approximate pairwise accuracy criterion
  • Chernoff criterion
  • Evolution strategies
  • Feature extraction
  • Linear discriminant analysis
  • Mahalanobis distance
  • Weighted LDA

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