Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix


  • 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



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.




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.



AAAI Technical Track: Machine Learning