Multi-View Clustering Based on Adaptive Tensor Singular Value Shrinkage

Qian Luoxiong; Chen Mei; Ma Xueyan; Zhang Chi; Zhang Jinhong

doi:10.7544/issn1000-1239.202330785

Journal of Computer Research and Development > 2025 > 62(3): 733-750. > DOI: 10.7544/issn1000-1239.202330785 CSTR: 32373.14.issn1000-1239.202330785

Qian Luoxiong, Chen Mei, Ma Xueyan, Zhang Chi, Zhang Jinhong. Multi-View Clustering Based on Adaptive Tensor Singular Value Shrinkage[J]. Journal of Computer Research and Development, 2025, 62(3): 733-750. DOI: 10.7544/issn1000-1239.202330785

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Multi-View Clustering Based on Adaptive Tensor Singular Value Shrinkage

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070

Funds: This work was supported by the National Natural Science Foundation of China (62266029), the Key Research and Development Program of Gansu Province (21YF5GA053), and the Higher Education Industry Support Program of Gansu Province (2022CYZC-36).

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Author Bio:
Qian Luoxiong: born in 1998. Master. His main research interests include complex data mining and graph representation clustering

Chen Mei: born in 1973. PhD, professor, PhD supervisor. Member of CCF. Her main research interests include complex data mining, social network analysis, and pattern recognition

Ma Xueyan: born in 1995. Master. Her main research interests include complex data mining and graph learning clustering

Zhang Chi: born in 1999. Master. His main research interests include complex data mining and time series clustering

Zhang Jinhong: born in 1998. Master. His main research interests include complex data mining and pattern recognition
Received Date: September 27, 2023
Revised Date: May 22, 2024
Accepted Date: August 08, 2024
Available Online: August 18, 2024

Graphical Abstract

Abstract

Abstract

The existing multi-view clustering algorithms exhibit limitations in accurately capturing the high-order information and complementary information embedded in multi-view data during the low-dimensional representations learning process. Meanwhile, these algorithms fail to capture the local information of data, and their information extraction methods lack robustness to noise and outliers. To address these challenges, an adaptive tensor singular value shrinkage multi-view clustering algorithm named ATSVS is proposed. ATSVS proposes a novel tensor log-determinant function to enforce the low-rank constraint on the representation tensor, which can adaptively enable adaptive shrinkage of singular values based on their magnitude. Consequently, ATSVS effectively captures high-order information and complementary information within multi-view data from the global perspective. Then, ATSVS captures the local information of the data by using the ${l_{1,2}}$ norm that combines the advantages of sparse representation and manifold regularization technology, while improving the robustness of the algorithm to noisy points by combining with ${l_{2,1}}$ norms to impose sparse constraints on the noise. The experimental results with eleven comparison algorithms on nine different types of datasets show that our proposed algorithm ATSVS has the superior clustering performance, outperforming state-of-the-art baselines significantly. Consequently, ATSVS is an excellent algorithm that can effectively handle the task of clustering multi-view data.
- tensor representation,
- clustering,
- low rank constraint,
- multi-view clustering,
- singular value decomposition

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References (36)

References

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