DWCL: Dual-weighted contrastive learning for robust multi-view clustering

Yuan, Hanning, Zhang, Zhihui, Guo, Qi, Chi, Lianhua, Ruan, Sijie, Zhou, Wei, Pang, Jinhui and Hao, Xiaoshuai 2026. DWCL: Dual-weighted contrastive learning for robust multi-view clustering. Engineering Applications of Artificial Intelligence 165 (PartB) , 113532. 10.1016/j.engappai.2025.113532

Full text not available from this repository.

Abstract

Multi-view contrastive clustering (MVCC) aims to learn consistent clustering structures from multiple views by maximizing the agreement between view-specific representations. However, existing methods often construct all pairwise cross-views indiscriminately, leading to numerous unreliable view combinations and representation degeneration. To address these issues, we propose Dual-Weighted Contrastive Learning (DWCL), a novel framework that selects the most reliable view using the silhouette coefficient and constructs targeted cross-views with other views via a Best-Other (B-O) contrastive mechanism. This strategy reduces the number of cross-views from quadratic to linear complexity, significantly improving computational efficiency. Additionally, we introduce a dual-weighting strategy that combines a view quality weight and a view discrepancy weight to adaptively emphasize high-quality, low-discrepancy cross-views. Extensive experiments on eight multi-view datasets demonstrate that DWCL consistently outperforms state-of-the-art methods. Specifically, DWCL achieves an absolute accuracy improvement of 3.5% on Caltech5V7 and 4.4% on CIFAR10. Theoretical analysis further validates the advantages of DWCL in improving mutual information bounds and reducing the influence of low-quality views. These results confirm that DWCL is a robust and efficient solution for scalable multi-view clustering.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2027-12-13
Publisher:	Elsevier
ISSN:	0952-1976
Date of Acceptance:	9 December 2025
Last Modified:	18 Dec 2025 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/183362

Actions (repository staff only)

Edit Item