Automatic coefficient selection in Weighted Maximum Margin Criterion

In this paper, one of the problems of linear discriminant analysis (LDA), that is, it pays more attention on minimizing the within-class scatter than on maximizing the between-class scatter, is treated. Though the weighted maximum margin criterion (WMMC) with an appropriate weighted coefficient can solve this problem, how to select this coefficient automatically is still difficult as most of previous works determine it manually. To deal with this problem, a novel approach to determine the coefficient automatically is proposed. The description and analysis of the approach are given in details, and the experiments on BernDBS face database and JAFFE expression database are performed. The results show that the WMMC with the weighted coefficients determined by this novel approach outperforms traditional LDA.