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.

翻译：多视图聚类（MVC）旨在以无监督方式将多视图数据划分为不同簇，是一项基础且具有挑战性的任务。为提升其在现实场景中的适用性，本文研究了一个更具挑战性的任务：多源噪声（包括缺失噪声和观测噪声）下的多视图聚类。为此，我们提出了一种新颖的框架——可靠性感知的对比深度多视图聚类（RAC-DMVC），该框架构建可靠性图以指导噪声环境下的鲁棒表示学习。具体而言，针对观测噪声，我们引入了跨视图重构以在数据层面增强鲁棒性，并采用可靠性感知的噪声对比学习来缓解由噪声表示引起的正负对选择偏差。为处理缺失噪声，我们设计了双重注意力填补机制，以捕获跨视图的共享信息，同时保留视图特异性特征。此外，自监督聚类蒸馏模块进一步优化了学习到的表示并提升了聚类性能。在五个基准数据集上的大量实验表明，RAC-DMVC在多项评估指标上优于当前最优方法，并在不同噪声比例下保持优异性能。