Binary sparse nonnegative matrix factorization

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

Research output: Contribution to journalArticlepeer-review


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
Issue number5
Publication statusPublished - May 2009


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


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