DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering

• URL: http://arxiv.org/abs/2411.17354v2
• Date: Thu, 23 Jan 2025 05:52:26 GMT
• Title: DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering
• Authors: Hanning Yuan, Zhihui Zhang, Qi Guo, Lianhua Chi, Sijie Ruan, Jinhui Pang, Xiaoshuai Hao,
• Abstract summary: We introduce a novel model called Dual-Weighted Contrastive Learning (DWCL) for Multi-View Clustering. Specifically, to reduce the impact of unreliable cross-views, we introduce an innovative Best-Other (B-O) contrastive mechanism. We develop a dual weighting strategy that combines a view quality weight, reflecting the quality of each view, with a view discrepancy weight.
• Score: 9.945837095280256
• License:
• Abstract: Multi-view contrastive clustering (MVCC) has gained significant attention for generating consistent clustering structures from multiple views through contrastive learning. However, most existing MVCC methods create cross-views by combining any two views, leading to a high volume of unreliable pairs. Furthermore, these approaches often overlook discrepancies in multi-view representations, resulting in representation degeneration. To address these challenges, we introduce a novel model called Dual-Weighted Contrastive Learning (DWCL) for Multi-View Clustering. Specifically, to reduce the impact of unreliable cross-views, we introduce an innovative Best-Other (B-O) contrastive mechanism that enhances the representation of individual views at a low computational cost. Furthermore, we develop a dual weighting strategy that combines a view quality weight, reflecting the quality of each view, with a view discrepancy weight. This approach effectively mitigates representation degeneration by downplaying cross-views that are both low in quality and high in discrepancy. We theoretically validate the efficiency of the B-O contrastive mechanism and the effectiveness of the dual weighting strategy. Extensive experiments demonstrate that DWCL outperforms previous methods across eight multi-view datasets, showcasing superior performance and robustness in MVCC. Specifically, our method achieves absolute accuracy improvements of 5.4\% and 5.6\% compared to state-of-the-art methods on the Caltech6V7 and MSRCv1 datasets, respectively.

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