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[2602.22568] Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise
Summary
The paper presents Quality-Aware Robust Multi-View Clustering (QARMVC), a novel framework addressing the challenges of heterogeneous observation noise in multi-view clustering, enhancing performance in real-world applications.
Why It Matters
This research is significant as it tackles the limitations of existing clustering methods that assume binary noise conditions. By introducing a quality-aware approach, it improves the robustness and accuracy of clustering in diverse data environments, which is crucial for applications in computer vision and artificial intelligence.
Key Takeaways
- QARMVC addresses the issue of heterogeneous observation noise in clustering.
- The framework employs an information bottleneck mechanism for better view reconstruction.
- Quality scores derived from reconstruction discrepancies enhance noise suppression.
- Extensive experiments show QARMVC outperforms existing methods in noisy conditions.
- The approach integrates quality-weighted strategies at both feature and fusion levels.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22568 (cs) [Submitted on 26 Feb 2026] Title:Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise Authors:Peihan Wu, Guanjie Cheng, Yufei Tong, Meng Xi, Shuiguang Deng View a PDF of the paper titled Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise, by Peihan Wu and 4 other authors View PDF HTML (experimental) Abstract:Deep multi-view clustering has achieved remarkable progress but remains vulnerable to complex noise in real-world applications. Existing noisy robust methods predominantly rely on a simplified binary assumption, treating data as either perfectly clean or completely corrupted. This overlooks the prevalent existence of heterogeneous observation noise, where contamination intensity varies continuously across data. To bridge this gap, we propose a novel framework termed Quality-Aware Robust Multi-View Clustering (QARMVC). Specifically, QARMVC employs an information bottleneck mechanism to extract intrinsic semantics for view reconstruction. Leveraging the insight that noise disrupts semantic integrity and impedes reconstruction, we utilize the resulting reconstruction discrepancy to precisely quantify fine-grained contamination intensity and derive instance-level quality scores. These scores are integrated into a hierarchical learning strategy: at the feature level, a quality-weighted contrastive objective is designed to adaptively suppress...