Binary sparse nonnegative matrix factorization

Yuan Yuan; Xuelong Li; Yanwei Pang; Xin Lu; Dacheng Tao

doi:10.1109/TCSVT.2009.2017306

Binary sparse nonnegative matrix factorization

Yuan Yuan, Xuelong Li, Yanwei Pang, Xin Lu, Dacheng Tao

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	4801604
Pages (from-to)	772-777
Number of pages	6
Journal	IEEE Transactions on Circuits and Systems For Video Technology
Volume	19
Issue number	5
DOIs	https://doi.org/10.1109/TCSVT.2009.2017306
Publication status	Published - May 2009

Keywords

binary principal component analysis
binary sparse nonnegative matrix factorization
face images
fast part-based subspace selection algorithm
image occlusions

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10.1109/TCSVT.2009.2017306

http://www.nlpr.ia.ac.cn/2009papers/kz/gk13.pdf

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title = "Binary sparse nonnegative matrix factorization",

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.",

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year = "2009",

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AU - Pang, Yanwei

AU - Lu, Xin

AU - Tao, Dacheng

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AB - 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.

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KW - binary sparse nonnegative matrix factorization

KW - face images

KW - fast part-based subspace selection algorithm

KW - image occlusions

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JO - IEEE Transactions on Circuits and Systems For Video Technology

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ER -

Binary sparse nonnegative matrix factorization

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