Supervised discriminant analysis derived from dissimilarity
Guanghua Chen · Computer Engineering and Applications Journal · 2011
This paper proposes a novel supervised discriminant analysis method derived from dissimilarity.Combining pattern local and global information,the new within-class and between-class scatter weight matrixes are defined.They represent dissimilarity of within-class sample and between-class sample respectively.The new within-class scatter and between-class scatter matrix are derived from the new scatter weight matrixes.The optimal transformation matrix is determined according to the Fisher criterion.The experimental results on YALE and AR face image database show that the proposed method outperforms the traditional approaches.