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

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 atthis https URLon Github.

@article{yang2025_2505.21387,
  title={ Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios },
  author={ Xihong Yang and Siwei Wang and Fangdi Wang and Jiaqi Jin and Suyuan Liu and Yue Liu and En Zhu and Xinwang Liu and Yueming Jin },
  journal={arXiv preprint arXiv:2505.21387},
  year={ 2025 }
}