Linear feature extraction by integrating pairwise and global discriminatory information via sequential forward floating selection and kernel QR factorization with column pivoting

Abstract

Linear discriminant analysis (LDA) is often used to produce an effective linear feature extractor for classification. However, some approaches of LDA, such as Fisher's linear discriminant, are not robust to outlier classes. In this paper, a novel approach is proposed to robustly produce an effective linear feature extractor by integrating the discriminatory information from the global and pairwise approaches of LDA. The discriminatory information is integrated either by the sequential forward floating selection algorithm with a criterion function based on the Chernoff bound or by ranking the discriminatory information using the kernel QR factorization with column pivoting according to the indication of an applicability index for these two methods. The proposed approach was compared to various methods of LDA. The experimental results have shown the robustness of the proposed approach and proved the feasibility of the proposed approach.