RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise
This addresses a challenging real-world problem in unsupervised data analysis for noisy multi-view data, representing an incremental improvement with specific gains.
The paper tackles multi-view clustering under multi-source noise, including missing and observation noise, by proposing RAC-DMVC, which outperforms state-of-the-art methods on five benchmark datasets and maintains performance under varying noise ratios.
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.