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Robust non-negative matrix factorization

  • Research Article
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Frontiers of Electrical and Electronic Engineering in China

Abstract

Non-negative matrix factorization (NMF) is a recently popularized technique for learning parts-based, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted, but the corruption is sparse. RNMF decomposes the non-negative data matrix as the summation of one sparse error matrix and the product of two non-negative matrices. An efficient iterative approach is developed to solve the optimization problem of RNMF. We present experimental results on two face databases to verify the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Lijun Zhang, Zhengguang Chen & Miao Zheng

  2. Xiaofei HE State Key Laboratory of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, 310058, China

    Xiaofei He

Authors
  1. Lijun Zhang
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  2. Zhengguang Chen
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  3. Miao Zheng
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  4. Xiaofei He
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Corresponding author

Correspondence to Lijun Zhang.

Additional information

Lijun ZHANG received the BS degree in Computer Science from Zhejiang University, China, in 2007. He is currently a candidate for a Ph.D. degree in Computer Science at Zhejiang University. His research interests include machine learning, information retrieval, and data mining.

Zhengguang CHEN received the BS degree in Computer Science from Zhejiang University, China, in 2009. He is currently a candidate for a MS degree in Computer Science at Zhejiang University. His research interests include computer vision, machine learning, and data mining.

Miao ZHENG received the BS degree in Computer Science from Zhejiang University, China, in 2008. He is currently a candidate for a Ph.D. degree in Computer Science at Zhejiang University. His research interests include machine learning, informa tion retrieval, and data mining.

Xiaofei HE received the BS degree in Computer Science from Zhejiang University, China, in 2000 and the Ph.D. degree in Computer Science from the University of Chicago, in 2005. He is a Professor in the State Key Lab of CAD&CG at Zhejiang University, China. Prior to joining Zhejiang University in 2007, he was a Research Scientist at Yahoo! Research Labs, Burbank, CA. His research interests include machine learning, information retrieval, and computer vision. He is a senior member of IEEE.

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Zhang, L., Chen, Z., Zheng, M. et al. Robust non-negative matrix factorization. Front. Electr. Electron. Eng. China 6, 192–200 (2011). https://doi.org/10.1007/s11460-011-0128-0

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  • DOI: https://doi.org/10.1007/s11460-011-0128-0

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