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International Conference on Neural Computing for Advanced Applications

NCAA 2022: Neural Computing for Advanced Applications pp 406–418Cite as

Multi-view Spectral Clustering with High-order Similarity Learning

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1637))

Abstract

Clustering objects with diverse attributes obtained from multiple views is full of challenges in fusing the multi-view information. Many of the present multi-view clustering (MVC) methods concentrate on direct similarity learning among data points and fail to excavate the hidden high-order similarity among different views. Therefore, it is difficult to obtain a dependable clustering assignments. To address this problem, we propose the high-order similarity (HOS) learning model for multi-view spectral clustering (MCHSL). The proposed MCHSL learns the first-order similarity (FOS), second-order similarity (SOS), and the HOS collaboratively to excavate the local structure relations, proximity structure relations of paired data points and the interactive-view relations among different views instead of the common similarity learning. Then spectral clustering is performed to obtain the final clustering assignments. Extensive experiments performed on some public datasets indicate that the proposed MCHSL has better clustering performance than benchmark methods in most cases and is able to reveal a dependable underlying similarity structure hidden in multiple views.

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Notes

  1. 1.

    http://www.cl.cam.ac.uk/research/dtg/.

  2. 2.

    http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

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Acknowledgement

This research was supported by the National Natural Science Foundation of China (Grant nos. 62106209).

Author information

Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Yanying Mei, Bin Wu & Yanhua Shao

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210008, China

    Zhenwen Ren

  3. School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhenwen Ren

Authors
  1. Yanying Mei
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  2. Zhenwen Ren
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  3. Bin Wu
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  4. Yanhua Shao
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Corresponding author

Correspondence to Zhenwen Ren .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. University of Jinan, Jinan, China

    Yuehui Chen

  3. Shenzhen University, Shenzhen, China

    Xianghua Chu

  4. Hefei University of Technology, Hefei, China

    Zhao Zhang

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

  6. Chongqing University, Chongqing, China

    Zhou Wu

  7. Western University, London, ON, Canada

    Yimin Yang

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mei, Y., Ren, Z., Wu, B., Shao, Y. (2022). Multi-view Spectral Clustering with High-order Similarity Learning. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_31

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  • DOI: https://doi.org/10.1007/978-981-19-6142-7_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6141-0

  • Online ISBN: 978-981-19-6142-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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