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

49 Scopus citations

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
State	Published - May 2009
Externally published	Yes

Keywords

Fast algorithms
Non-negative matrix factorization
Part-based representation
Sparseness
Subspace selection

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10.1109/TCSVT.2009.2017306

Cite this

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keywords = "Fast algorithms, Non-negative matrix factorization, Part-based representation, Sparseness, Subspace selection",

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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.

KW - Fast algorithms

KW - Non-negative matrix factorization

KW - Part-based representation

KW - Sparseness

KW - Subspace selection

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Binary sparse nonnegative matrix factorization

Abstract

Keywords

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