Discriminative Locality Alignment

—This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing process of B-SNMF are much faster than those of binary principal component analysis (B-PCA). Besides, B-SNMF is more robust to occlusions in images. Experimental results on face images demonstrate the effectiveness and the efficiency of the proposed B-SNMF.

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Tianhao Zhang, Dacheng Tao, Jie Yang
• Keywords: Binary Principal Component, Computer Vision, ECCV 2008, Nonnegative Matrix Factorization, Subspace Selection Algorithm