RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise

• URL: http://arxiv.org/abs/2511.13561v1
• Date: Mon, 17 Nov 2025 16:28:12 GMT
• Title: RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise
• Authors: Shihao Dong, Yue Liu, Xiaotong Zhou, Yuhui Zheng, Huiying Xu, Xinzhong Zhu,
• Abstract summary: Multi-view clustering (MVC) aims to separate the multi-view data into distinct clusters in an unsupervised manner.<n>This paper addresses a more challenging task: MVC under multi-source noises, including missing noise and observation noise.<n>We propose a novel framework, Reliability-Aware Contrastive Deep Multi-View Clustering (RAC-DMVC), which constructs a reliability graph to guide robust representation learning under noisy environments.
• Score: 32.96137519578033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view clustering (MVC), which aims to separate the multi-view data into distinct clusters in an unsupervised manner, is a fundamental yet challenging task. To enhance its applicability in real-world scenarios, this paper addresses a more challenging task: MVC under multi-source noises, including missing noise and observation noise. To this end, we propose a novel framework, Reliability-Aware Contrastive Deep Multi-View Clustering (RAC-DMVC), which constructs a reliability graph to guide robust representation learning under noisy environments. Specifically, to address observation noise, we introduce a cross-view reconstruction to enhances robustness at the data level, and a reliability-aware noise contrastive learning to mitigates bias in positive and negative pairs selection caused by noisy representations. To handle missing noise, we design a dual-attention imputation to capture shared information across views while preserving view-specific features. In addition, a self-supervised cluster distillation module further refines the learned representations and improves the clustering performance. Extensive experiments on five benchmark datasets demonstrate that RAC-DMVC outperforms SOTA methods on multiple evaluation metrics and maintains excellent performance under varying ratios of noise.

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