Binary sparse nonnegative matrix factorization

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

Research output: Contribution to journalArticle

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 languageEnglish
Article number4801604
Pages (from-to)772-777
Number of pages6
JournalIEEE Transactions on Circuits and Systems For Video Technology
Volume19
Issue number5
DOIs
Publication statusPublished - May 2009

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Factorization
Principal component analysis
Testing

Keywords

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

Cite this

Yuan, Yuan ; Li, Xuelong ; Pang, Yanwei ; Lu, Xin ; Tao, Dacheng. / Binary sparse nonnegative matrix factorization. In: IEEE Transactions on Circuits and Systems For Video Technology. 2009 ; Vol. 19, No. 5. pp. 772-777.
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Binary sparse nonnegative matrix factorization. / Yuan, Yuan; Li, Xuelong; Pang, Yanwei; Lu, Xin; Tao, Dacheng.

In: IEEE Transactions on Circuits and Systems For Video Technology, Vol. 19, No. 5, 4801604, 05.2009, p. 772-777.

Research output: Contribution to journalArticle

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