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

arXiv: https://arxiv.org/abs/2511.13561 · HTML: https://mlsanity.com/paper/2511.13561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Decoupled Contrastive Multi-View Clustering with High-Order Random Walks](https://mlsanity.com/paper/2308.11164/) (2023)
- [Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios](https://mlsanity.com/paper/2303.17245/) (2023)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and Prediction](https://mlsanity.com/paper/2501.07157/) (2025)
- [Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios](https://mlsanity.com/paper/2505.21387/) (2025)

## Built on by (most cited first, 1 of 1)

- [Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise](https://mlsanity.com/paper/2602.22568/) (2026)

## Abstract (arXiv)

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.