Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise
#Multi-view clustering #Heterogeneous noise #Quality-aware framework #Information bottleneck #Deep learning #Computer vision
📌 Key Takeaways
- Existing multi-view clustering methods use simplified binary assumptions about data cleanliness
- QARMVC framework uses information bottleneck mechanism to extract intrinsic semantics
- The system measures reconstruction discrepancy to quantify contamination intensity
- Experiments on five benchmark datasets show superior performance, especially with heterogeneous noise
📖 Full Retelling
Researchers Peihan Wu, Guanjie Cheng, Yufei Tong, Meng Xi, and Shuiguang Deng introduced a novel framework called Quality-Aware Robust Multi-View Clustering (QARMVC) in a paper submitted to arXiv on February 26, 2026, to address the limitation of existing methods in handling heterogeneous observation noise in multi-view clustering algorithms. The paper highlights that while deep multi-view clustering has made significant progress, it remains vulnerable to complex noise in real-world applications, with current approaches relying on oversimplified binary assumptions about data cleanliness. The QARMVC framework employs an information bottleneck mechanism to extract intrinsic semantics for view reconstruction, utilizing the insight that noise disrupts semantic integrity and impedes reconstruction. By measuring reconstruction discrepancy, the system can precisely quantify fine-grained contamination intensity and derive instance-level quality scores. These scores are then integrated into a hierarchical learning strategy that includes a quality-weighted contrastive objective at the feature level and a quality-weighted aggregation method at the fusion level to construct high-quality global consensus and align local views through mutual information maximization.
🏷️ Themes
Computer Vision, Artificial Intelligence, Data Clustering
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--> Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22568 [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 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 . 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 the propagation of noise; at the fusion level, a high-quality global consensus is constructed via quality-weighted aggregation, which is subsequently utilized to align and rectify local views via mutual information maximization. Extensive experiments on five benchmark datasets demonstrate that QARMVC consistently outperforms state-of-the-art baselines, particularly in scenarios with heterogeneous noise intensities. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as:...
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