Multiview Tensor Spectral Clustering via Co-regularization

Hongmin Cai, Yu Wang, Fei Qi, Zhuoyao Wang, Yiu-ming Cheung*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Graph-based multi-view clustering encodes multi-view data into sample affinities to find consensus representation, effectively overcoming heterogeneity across different views. However, traditional affinity measures tend to collapse as the feature dimension expands, posing challenges in estimating a unified alignment that reveals both crossview and inner relationships. To tackle this challenge, we propose to achieve multi-view uniform clustering via consensus representation coregularization. First, the sample affinities are encoded by both popular dyadic affinity and recent high-order affinities to comprehensively characterize spatial distributions of the HDLSS data. Second, a fused consensus representation is learned through aligning the multi-view lowdimensional representation by co-regularization. The learning of the fused representation is modeled by a high-order eigenvalue problem within manifold space to preserve the intrinsic connections and complementary correlations of original data. A numerical scheme via manifold minimization is designed to solve the high-order eigenvalue problem efficaciously. Experiments on eight HDLSS datasets demonstrate the effectiveness of our proposed method in comparison with the recent thirteen benchmark methods.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
DOIs
Publication statusE-pub ahead of print - 9 Apr 2024

Scopus Subject Areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

User-Defined Keywords

  • Clustering
  • Correlation
  • Distribution functions
  • Eigenvalues and eigenfunctions
  • Fusing affinity
  • Graphical models
  • High-order affinity
  • Laplace equations
  • Manifold optimization
  • Manifolds
  • Spectral graph
  • Tensor
  • Tensors

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