A linear discriminant analysis using weighted local structure information

Abstract

The linear discriminant analysis (LDA) is one of the most efficient supervised dimensionality reduction technique widely used in face recognition. This paper proposed a new weighted LDA to improve the performance of the discriminant analysis. Confusable pair of classes is considered as the primary goal in our objective function. The proposed technique not only improves the minimization of the within-class scatter, but also improves the maximization of the between classes scatter to extract better discriminant feature subset. The experimental results a real word dataset demonstrate that the proposed method achieve higher recognition rate than that traditional LDA as well as other weighted LDA.