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Linear dimensionality reduction using relevance weighted LDA. (English) Zbl 1079.68613
Summary: 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.

MSC:
68T10 Pattern recognition, speech recognition
Software:
UCI-ml
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