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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2017: Intelligence Science and Big Data Engineering pp 461–470Cite as

Subspace Clustering Under Multiplicative Noise Corruption

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Traditional subspace clustering models generally adopt the hypothesis of additive noise, which, however, dose not always hold. When it comes to multiplicative noise corruption, these models usually have poor performance. Therefore, we propose a novel model for robust subspace clustering with multiplicative noise corruption to alleviate this problem, which is the key contribution of this work. The proposed model is evaluated on the Extend Yale B and MNIST datasets and the experimental results show that our method achieves favorable performance against the state-of-the-art methods.

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, China

    Baohua Li & Wei Wu

Authors
  1. Baohua Li
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  2. Wei Wu
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Corresponding author

Correspondence to Baohua Li .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Li, B., Wu, W. (2017). Subspace Clustering Under Multiplicative Noise Corruption. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_41

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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