Binary sparse nonnegative matrix factorization

Authors:
Yuan Yuan;Xuelong Li;Yanwei Pang;Xin Lu;Dacheng Tao
Affiliations:
School of Engineering and Applied Science, Aston University, Birmingham, UK;School of Computer Science and Information Systems, Birkbeck College, University of London, London, UK;School of Electronic Information Engineering, Tianjin University, Tianjin, China;School of Electronic Information Engineering, Tianjin University, Tianjin, China;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2009

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Abstract

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.