• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation

Vol. 16, No. 3, March 31, 2022
10.3837/tiis.2022.03.004, Download Paper (Free):

Abstract

In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.


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Cite this article

[IEEE Style]
Y. Long, X. Liu and S. Murphy, "Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation," KSII Transactions on Internet and Information Systems, vol. 16, no. 3, pp. 813-829, 2022. DOI: 10.3837/tiis.2022.03.004.

[ACM Style]
Yin Long, Xiaobo Liu, and Simon Murphy. 2022. Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation. KSII Transactions on Internet and Information Systems, 16, 3, (2022), 813-829. DOI: 10.3837/tiis.2022.03.004.

[BibTeX Style]
@article{tiis:25518, title="Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation", author="Yin Long and Xiaobo Liu and Simon Murphy and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.03.004}, volume={16}, number={3}, year="2022", month={March}, pages={813-829}}