Projection-optimal tensor local fisher discriminant analysis for image feature extraction

Tensor-based feature extraction approaches have been proved to be effective since they can solve the undersampled problem. In this paper, we propose a novel method called projection-optimal tensor local fisher discriminant analysis (PoTLFDA), which shares the character of local fisher discriminant analysis (LFDA). A novel affinity matrix is defined to effectively reflect the relationships of points in original tensor space and embedding space. The projection matrices are optimized by alternately solving the trace ratio problem. Convergence proof of the proposed algorithm is also given in this paper. Experiment results on face databases demonstrate the effectiveness of PoTLFDA.