Tensor-Based Uncoupled and Incomplete Multi-View Clustering

Yapeng Liu, Wei Guo, Weiyu Li, Jingfeng Su, Qianlong Zhou and Shanshan Yu ()
Additional contact information
Yapeng Liu: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Wei Guo: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Weiyu Li: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Jingfeng Su: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Qianlong Zhou: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Shanshan Yu: Training and Basic Education Management Office, Southwest University, Chongqing 400715, China

Mathematics, 2025, vol. 13, issue 9, 1-23

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/9/1516/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/9/1516/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:9:p:1516-:d:1649210

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().