Generalization of linear discriminant analysis using Lp-norm

Authors:
Jae Hyun Oh;Nojun Kwak
Affiliations:
Department of Electrical & Computer Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Republic of Korea;Department of Electrical & Computer Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon 443-749, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

In this paper, the linear discriminant analysis (LDA) is generalized by using an L"p-norm optimization technique. Although conventional LDA based on the L"2-norm has been successful for many classification problems, performances can degrade with the presence of outliers. The effect of outliers which is exacerbated by the use of the L"2-norm can cause this phenomenon. To cope with this problem, we propose an LDA based on the L"p-norm optimization technique (LDA-L"p), which is robust to outliers. Arbitrary values of p can be used in this scheme. The experimental results show that the proposed method achieves high recognition rate for many datasets. The reason for the performance improvements is also analyzed.