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Multi-view Spectral Clustering via Multi-view Weighted Consensus and Matrix-Decomposition Based Discretization

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Database Systems for Advanced Applications (DASFAA 2019)

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Abstract

In recent years, multi-view clustering has been widely used in many areas. As an important category of multi-view clustering, multi-view spectral clustering has recently shown promising advantages in partitioning clusters of arbitrary shapes. Despite significant success, there are still two challenging issues in multi-view spectral clustering, i.e., (i) how to learn a similarity matrix for multiple weighted views and (ii) how to learn a robust discrete clustering result from the (continuous) eigenvector domain. To simultaneously tackle these two issues, this paper proposes a unified spectral clustering approach based on multi-view weighted consensus and matrix-decomposition based discretization. In particular, a multi-view consensus similarity matrix is first learned with the different views weighted w.r.t. their confidence. Then the eigen-decomposition is performed on the similarity matrix and a set of c eigenvectors are obtained. From the eigenvectors, we first learn a continuous cluster label and then discretize it to build the final clustering label, which avoids the potential instability of the conventional k-means discretization. Extensive experiments have been conducted on multiple multi-view datasets to validate the superiority of our proposed approach.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/index.php.

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Acknowledgments

This work was supported by NSFC (61876193, 61602189), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).

Author information

Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Man-Sheng Chen, Ling Huang & Chang-Dong Wang

  2. Guangdong Province Key Laboratory of Computational Science, Guangzhou, China

    Man-Sheng Chen, Ling Huang & Chang-Dong Wang

  3. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Dong Huang

Authors
  1. Man-Sheng Chen
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  2. Ling Huang
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  3. Chang-Dong Wang
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  4. Dong Huang
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Corresponding author

Correspondence to Chang-Dong Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Chen, MS., Huang, L., Wang, CD., Huang, D. (2019). Multi-view Spectral Clustering via Multi-view Weighted Consensus and Matrix-Decomposition Based Discretization. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-18576-3_11

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