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Tensor Linear Discriminant Analysis
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Author(s): David Zhang (Hong Kong Polytechnic University, Hong Kong), Fengxi Song (New Star Research Institute Of Applied Technology, China), Yong Xu (Harbin Institute of Technology, China)and Zhizhen Liang (Shanghai Jiao Tong University, China)
Copyright: 2009
Pages: 30
Source title:
Advanced Pattern Recognition Technologies with Applications to Biometrics
Source Author(s)/Editor(s): David Zhang (Hong Kong Polytechnic University, Hong Kong ), Fengxi Song (New Star Research Institute Of Applied Technology, China), Yong Xu (Harbin Institute of Technology, China)and Zhizhen Liang (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60566-200-8.ch009
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Abstract
Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In this chapter, classical LDA and its several variants are introduced. In some sense, the variants of LDA can avoid the singularity problem and achieve computational efficiency. Experimental results on biometric data show the usefulness of LDA and its variants in some cases.
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