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Mathematics
Volume 13
Issue 9
10.3390/math13091516
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Tensor-Based Uncoupled and Incomplete Multi-View Clustering

by
Yapeng Liu
1,
Wei Guo
1,
Weiyu Li
1,
Jingfeng Su
1,
Qianlong Zhou
1 and
Shanshan Yu
2,3,*
1
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
2
Training and Basic Education Management Office, Southwest University, Chongqing 400715, China
3
Key Laboratory of Cyber-Physical Fusion Intelligent Computing (South-Central Minzu University), State Ethnic Affairs Commission, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(9), 1516; https://doi.org/10.3390/math13091516
Submission received: 18 March 2025 / Revised: 23 April 2025 / Accepted: 29 April 2025 / Published: 4 May 2025
Versions Notes

Abstract

Multi-view clustering demonstrates strong performance in various real-world applications. However, real-world data often contain incomplete and uncoupled views. Missing views can lead to the loss of latent information, and uncoupled views create obstacles for cross-view learning. Existing methods rarely consider incomplete and uncoupled multi-view data simultaneously. To address these problems, a novel method called Tensor-based Uncoupled and Incomplete Multi-view Clustering (TUIMC) is proposed to effectively handle incomplete and uncoupled data. Specifically, the proposed method recovers missing samples in a low-dimensional feature space. Subsequently, the self-representation matrices are paired with the optimal views through permutation matrices. The coupled self-representation matrices are integrated into a third-order tensor to explore high-order information of multi-view data. An efficient algorithm is designed to solve the proposed model. Experimental results on five widely used benchmark datasets show that the proposed method exhibits superior clustering performance on incomplete and uncoupled multi-view data.
Keywords: multi-view clustering; uncoupled views; incomplete data; cross-view learning; self-representation multi-view clustering; uncoupled views; incomplete data; cross-view learning; self-representation

Share and Cite

MDPI and ACS Style

Liu, Y.; Guo, W.; Li, W.; Su, J.; Zhou, Q.; Yu, S. Tensor-Based Uncoupled and Incomplete Multi-View Clustering. Mathematics 2025, 13, 1516. https://doi.org/10.3390/math13091516

AMA Style

Liu Y, Guo W, Li W, Su J, Zhou Q, Yu S. Tensor-Based Uncoupled and Incomplete Multi-View Clustering. Mathematics. 2025; 13(9):1516. https://doi.org/10.3390/math13091516

Chicago/Turabian Style

Liu, Yapeng, Wei Guo, Weiyu Li, Jingfeng Su, Qianlong Zhou, and Shanshan Yu. 2025. "Tensor-Based Uncoupled and Incomplete Multi-View Clustering" Mathematics 13, no. 9: 1516. https://doi.org/10.3390/math13091516

APA Style

Liu, Y., Guo, W., Li, W., Su, J., Zhou, Q., & Yu, S. (2025). Tensor-Based Uncoupled and Incomplete Multi-View Clustering. Mathematics, 13(9), 1516. https://doi.org/10.3390/math13091516

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