Generalization of linear discriminant analysis using <i>L<sub>p</sub></i>-norm

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. (C) 2013 Elsevier B.V. All rights reserved.