# Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios

arXiv: https://arxiv.org/abs/2505.21387 · HTML: https://mlsanity.com/paper/2505.21387/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 64 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 28 of 28)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [Simple Contrastive Graph Clustering](https://mlsanity.com/paper/2205.07865/) (2022)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Generative Partial Multi-View Clustering](https://mlsanity.com/paper/2003.13088/) (2020)
- [Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning](https://mlsanity.com/paper/2202.11915/) (2022)
- [CONVERT:Contrastive Graph Clustering with Reliable Augmentation](https://mlsanity.com/paper/2308.08963/) (2023)
- [Decoupled Contrastive Multi-View Clustering with High-Order Random Walks](https://mlsanity.com/paper/2308.11164/) (2023)
- [Mixed Graph Contrastive Network for Semi-Supervised Node Classification](https://mlsanity.com/paper/2206.02796/) (2022)
- [Cross-domain recommendation via user interest alignment](https://mlsanity.com/paper/2301.11467/) (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)
- [DealMVC: Dual Contrastive Calibration for Multi-view Clustering](https://mlsanity.com/paper/2308.09000/) (2023)
- [Dataset Regeneration for Sequential Recommendation](https://mlsanity.com/paper/2405.17795/) (2024)
- [Dual Test-time Training for Out-of-distribution Recommender System](https://mlsanity.com/paper/2407.15620/) (2024)
- [Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization](https://mlsanity.com/paper/1809.05998/) (2018)
- [DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System](https://mlsanity.com/paper/2408.08231/) (2024)
- [GraphLearner: Graph Node Clustering with Fully Learnable Augmentation](https://mlsanity.com/paper/2212.03559/) (2022)
- [Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2504.04843/) (2025)
- [Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective](https://mlsanity.com/paper/2412.00742/) (2024)
- [Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information](https://mlsanity.com/paper/2309.00474/) (2023)
- [HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters](https://mlsanity.com/paper/2411.01155/) (2024)

## Built on by (most cited first, 3 of 3)

- [Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence](https://mlsanity.com/paper/2509.16022/) (2025)
- [RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise](https://mlsanity.com/paper/2511.13561/) (2025)
- [Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise](https://mlsanity.com/paper/2602.22568/) (2026)

## Abstract (arXiv)

Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as an anomaly identification problem using GMM. We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results. Furthermore, we introduce a noise-robust contrastive mechanism to generate reliable representations. Additionally, we provide a theoretical proof demonstrating that these representations can discard noisy information, thereby improving the performance of downstream tasks. Extensive experiments on six benchmark datasets demonstrate that AIRMVC outperforms state-of-the-art algorithms in terms of robustness in noisy scenarios. The code of AIRMVC are available at https://github.com/xihongyang1999/AIRMVC on Github.