Discriminant Criteria for Pattern Classification

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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: 28
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.ch003

Keywords: Biometrics / Biotechnology and Bioinformatics / Engineering Science Reference / Medicine & Healthcare

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Abstract

As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book, we focus our attention on the feature extraction aspect of LDA for SSS problems. On the other hand,, with this chapter we present our studies on the pattern classification aspect of LDA for SSS problems. In this chapter, we present three novel classification-oriented linear discriminant criteria. The first one is large margin linear projection (LMLP) which makes full use of the characteristic of the SSS problems. The second one is the minimum norm minimum squared-error criterion which is a modification of the minimum squared-error discriminant criterion. The third one is the maximum scatter difference which is a modification of the Fisher discriminant criterion.

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