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

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.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning