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Incremental Nonnegative Matrix Factorization with Sparseness Constraint for Image Representation

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Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11165))

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Abstract

Nonnegative matrix factorization (NMF) is a powerful method of data dimension reduction and has been widely used in face recognition. However, existing NMF algorithms have two main drawbacks. One is that the speed is too slow for large matrix factorization. The other is that it must conduct repetitive learning when the training samples or classes are incremental. In order to overcome these two limitations and improve the sparseness of the data after factorization, this paper presents a novel algorithm, which is called incremental nonnegative matrix factorization with sparseness constraint. By using the results of previous factorization involved in iterative computation with sparseness constraint, the cost of computation is reduced and the sparseness of data after factorization is greatly improved. Compared with NMF and INMF, the experimental results on some face databases have shown that the proposed method achieves superior results.

Supported by the National Natural Science Foundation of China (NSFC) under Grants 61572244, 61472059 and 61772108.

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Author information

Authors and Affiliations

  1. Dalian University of Technology, Dalian, 116300, China

    Jing Sun, Zhihui Wang & Haojie Li

  2. Liaoning University of Technology, Jinzhou, 121001, China

    Fuming Sun

Authors
  1. Jing Sun
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  2. Zhihui Wang
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  3. Haojie Li
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  4. Fuming Sun
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Corresponding author

Correspondence to Haojie Li .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Sun, J., Wang, Z., Li, H., Sun, F. (2018). Incremental Nonnegative Matrix Factorization with Sparseness Constraint for Image Representation. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-00767-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00766-9

  • Online ISBN: 978-3-030-00767-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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