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A Nonnegative Projection Based Algorithm for Low-Rank Nonnegative Matrix Approximation

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

Nonnegative matrix factorization/approximation (NMF/NMA) is a widely used method for data analysis. So far, many multiplicative update algorithms have been developed for NMF. In this paper, we propose a nonnegative projection based NMF algorithm, which is different from the conventional multiplicative update NMF algorithms and decreases the objective function by performing Procrustes rotation and nonnegative projection alternately. The experiment results demonstrate that the new algorithm converges much faster than traditional ones.

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Acknowledgments

The work was supported in part by Guangzhou Science and Technology Program under Grant 201508010007.

Author information

Authors and Affiliations

  1. School of Automation and Institute of Intelligent Signal Processing, Guangdong University of Technology, Guangzhou, 510006, China

    Peitao Wang, Zhaoshui He & Kan Xie

  2. Discipline of Business Analytics, The University of Sydney Business School, Camperdown, NSW, 2006, Australia

    Junbin Gao

  3. School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Michael Antolovich

Authors
  1. Peitao Wang
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  2. Zhaoshui He
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  3. Kan Xie
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  4. Junbin Gao
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  5. Michael Antolovich
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Corresponding author

Correspondence to Zhaoshui He .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Wang, P., He, Z., Xie, K., Gao, J., Antolovich, M. (2017). A Nonnegative Projection Based Algorithm for Low-Rank Nonnegative Matrix Approximation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

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