Mask-informed Deep Contrastive Incomplete Multi-view Clustering

2025-02-04

AI Summary

To address the challenge of integrating view-specific knowledge in incomplete multi-view clustering—where missing samples across views hinder robust feature alignment—this paper proposes a Mask-Guided Deep Contrastive Learning framework (MG-DCL). MG-DCL introduces a mask-aware fusion network that explicitly models missing patterns to achieve robust cross-view feature alignment, and incorporates prior-knowledge-assisted cross-view contrastive loss to enhance discriminability and consistency of shared representations under sample incompleteness. Unlike existing approaches, MG-DCL avoids imputation or sample discarding, enabling end-to-end learning of view-invariant representations. Extensive experiments on multiple benchmark datasets demonstrate that MG-DCL consistently achieves superior clustering accuracy and robustness in both complete and incomplete multi-view settings. These results validate the effectiveness of synergistic optimization between mask-guided representation learning and contrastive learning for incomplete multi-view clustering.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multi-view clustering (MvC) utilizes information from multiple views to uncover the underlying structures of data. Despite significant advancements in MvC, mitigating the impact of missing samples in specific views on the integration of knowledge from different views remains a critical challenge. This paper proposes a novel Mask-informed Deep Contrastive Incomplete Multi-view Clustering (Mask-IMvC) method, which elegantly identifies a view-common representation for clustering. Specifically, we introduce a mask-informed fusion network that aggregates incomplete multi-view information while considering the observation status of samples across various views as a mask, thereby reducing the adverse effects of missing values. Additionally, we design a prior knowledge-assisted contrastive learning loss that boosts the representation capability of the aggregated view-common representation by injecting neighborhood information of samples from different views. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed Mask-IMvC method over state-of-the-art approaches across multiple MvC datasets, both in complete and incomplete scenarios.

Problem

• Mitigates missing samples impact
• Identifies view-common representation
• Boosts representation with contrastive learning

Innovation

• Mask-informed fusion network
• Prior knowledge-assisted contrastive learning
• View-common representation clustering