Linear dimensionality reduction using relevance weighted LDA

Authors:
E. K. Tang;P. N. Suganthan;X. Yao;A. K. Qin
Affiliations:
School of Electrical and Electronic Engineering Nanyang Technological University, Nanyang Avenue, Block S2, Singapore 639798, Singapore;School of Electrical and Electronic Engineering Nanyang Technological University, Nanyang Avenue, Block S2, Singapore 639798, Singapore;School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom;School of Electrical and Electronic Engineering Nanyang Technological University, Nanyang Avenue, Block S2, Singapore 639798, Singapore
Venue:
Pattern Recognition
Year:
2005

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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.