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Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

PRCV 2019: Pattern Recognition and Computer Vision pp 75–83Cite as

Pseudo Label Guided Subspace Learning for Multi-view Data

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11859))

Abstract

Multi-view spectral clustering methods could utilize the complementary information from different views to increase the robustness of clustering performances. Graph structures are usually revealed as affinity matrices. A pseudo label guided spectral embedding algorithm (PLGS) is proposed in this paper to enhance the consistence between graph matrices and spectral clustering results. Through iteratively estimating the pseudo labels of all samples and similarity matrices, the cluster assignment vector could be calculated with more confidence. Extensive experimental results on several benchmark datasets show promising performance and verify the effectiveness of our method.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/datasets/Multiple+Features.

  2. 2.

    http://mlg.ucd.ie/datasets/3sources.html.

  3. 3.

    http://www.escience.cn/people/fpnie/papers.html.

  4. 4.

    https://github.com/mbrbic/MultiViewLRSSC.

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Acknowledgements

This work is partly supported in part by Natural Science Foundation of Anhui Province under Grant 1808085QF210 and Grant 1608085MF129. And in part by the Major and Key Project of Natural Science of Anhui Provincial Department of Education under Grant KJ2015ZD09 and Grant KJ2018A0043.

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Authors and Affiliations

  1. Anhui University of Technology, Maanshan, 340122, China

    Shudong Hou, Heng Liu & Xiujun Wang

Authors
  1. Shudong Hou
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  2. Heng Liu
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  3. Xiujun Wang
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Corresponding author

Correspondence to Shudong Hou .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

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

    Liang Wang

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

    Jian Yang

  4. Xidian University, Xi’an, China

    Guangming Shi

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

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi’an, China

    Yanning Zhang

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Cite this paper

Hou, S., Liu, H., Wang, X. (2019). Pseudo Label Guided Subspace Learning for Multi-view Data. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-31726-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31725-6

  • Online ISBN: 978-3-030-31726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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