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Complexity-Reduced Scheme for Feature Extraction With Linear Discriminant Analysis

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4 Author(s)
Yuxi Hou ; Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea ; Iickho Song ; Hwang-Ki Min ; Cheol Hoon Park

Owing to the singularity of the within-class scatter, linear discriminant analysis (LDA) becomes ill-posed for small sample size (SSS) problems. Null-space-based LDA (NLDA), which is an extension of LDA, provides good discriminant performances for SSS problems. Yet, as the original scheme for the feature extractor (FE) of NLDA suffers from a complexity burden, a few modified schemes have since been proposed for complexity reduction. In this brief, by transforming the problem of finding the FE of NLDA into a linear equation problem, a novel scheme is derived, offering a further reduction of the complexity.

Published in:

IEEE Transactions on Neural Networks and Learning Systems  (Volume:23 ,  Issue: 6 )