Dual contrastive learning with graph masking: A self-supervised framework for multi-view clustering.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Mining

Background:

• Graph-based deep multi-view clustering effectively models complex relationships but suffers from noise sensitivity and neglects inter-view heterogeneity.
• Existing methods often lead to representation homogenization and significant consistency loss due to high masking ratios.

Purpose of the Study:

• To propose Dual Contrastive Masked Graph-Autoencoder Learning (DCMGAL) for robust multi-view clustering.
• To address limitations of noise, redundancy, and inter-view heterogeneity in existing graph-based clustering approaches.

Main Methods:

• Incorporates a masked aggregation module with randomized edge masking to amplify discrepancies and suppress noise.
• Employs a global feature fusion mechanism with dual attention and self-expression networks for complementary information.
• Utilizes a dual contrastive learning module for cluster-level consistency and local topology preservation.
• Includes an adjacency graph reconstruction component via a graph autoencoder to retain neighborhood information.

Main Results:

• DCMGAL effectively amplifies inter-view discrepancies while suppressing noise and redundancy.
• The dual contrastive learning module enhances representation separability by enforcing consistency and preserving local topology.
• Extensive experiments demonstrate DCMGAL's superior performance over state-of-the-art clustering methods on benchmark datasets.

Conclusions:

• DCMGAL offers a robust solution for multi-view clustering by effectively handling noise and heterogeneity.
• The proposed method achieves significant improvements in clustering performance through its innovative architecture and learning strategies.