Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix

Authors

  • Sihang Zhou NUDT
  • Xinwang Liu NUDT
  • Jiyuan Liu NUDT
  • Xifeng Guo NUDT
  • Yawei Zhao NUDT
  • En Zhu NUDT
  • Yongping Zhai NUDT
  • Jianping Yin DGUT
  • Wen Gao PKU

DOI:

https://doi.org/10.1609/aaai.v34i04.6180

Abstract

Multi-view spectral clustering aims to group data into different categories by optimally exploring complementary information from multiple Laplacian matrices. However, existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct an optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. In this paper, we propose a novel optimal neighborhood multi-view spectral clustering (ONMSC) algorithm to address these issues. Specifically, the proposed algorithm generates an optimal Laplacian matrix by searching the neighborhood of both the linear combination of the first-order and high-order base Laplacian matrices simultaneously. This design enhances the representative capacity of the optimal Laplacian and better utilizes the hidden high-order connection information, leading to improved clustering performance. An efficient algorithm with proved convergence is designed to solve the resultant optimization problem. Extensive experimental results on 9 datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which verifies the effectiveness and advantages of the proposed ONMSC.

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Published

2020-04-03

How to Cite

Zhou, S., Liu, X., Liu, J., Guo, X., Zhao, Y., Zhu, E., Zhai, Y., Yin, J., & Gao, W. (2020). Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6965-6972. https://doi.org/10.1609/aaai.v34i04.6180

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Section

AAAI Technical Track: Machine Learning