4.7 Article

Rank Consistency Induced Multiview Subspace Clustering via Low-Rank Matrix Factorization

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3071797

关键词

Sparse matrices; Optimization; Clustering algorithms; Clustering methods; Tensors; Matrix decomposition; Learning systems; Complementary information; low-rank representation (LRR); low-rank matrix factorization; multiview subspace clustering; rank consistency

资金

  1. National Natural Science Foundation of China [61772048, U19B2039, U1811463, 61806014, 61632006]
  2. Beijing Talents Project [2017A24]

向作者/读者索取更多资源

This article introduces a novel multiview subspace clustering model based on rank consistency, aiming to enhance structural consistency and fully utilize complementary information by parameterizing low-rank structure on all self-expressiveness coefficient matrices. Extensive experiments show the advantage of the proposed model over state-of-the-art multiview clustering approaches.
Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used in most existing methods is to learn a shared self-expressiveness coefficient matrix for all the view data. Different from such a strategy, this article proposes a rank consistency induced multiview subspace clustering model to pursue a consistent low-rank structure among view-specific self-expressiveness coefficient matrices. To facilitate a practical model, we parameterize the low-rank structure on all self-expressiveness coefficient matrices through the tri-factorization along with orthogonal constraints. This specification ensures that self-expressiveness coefficient matrices of different views have the same rank to effectively promote structural consistency across multiviews. Such a model can learn a consistent subspace structure and fully exploit the complementary information from the view-specific self-expressiveness coefficient matrices, simultaneously. The proposed model is formulated as a nonconvex optimization problem. An efficient optimization algorithm with guaranteed convergence under mild conditions is proposed. Extensive experiments on several benchmark databases demonstrate the advantage of the proposed model over the state-of-the-art multiview clustering approaches.

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