Binary sparse nonnegative matrix factorization

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Journal Article
IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19 (5), pp. 772 - 777
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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. © 2009 IEEE.
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