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

• URL: http://arxiv.org/abs/2505.21387v1
• Date: Tue, 27 May 2025 16:16:54 GMT
• Title: Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
• Abstract summary: We propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC.<n>Specifically, we reformulate noisy identification as an anomaly identification problem using GMM.<n>We then design a hybrid rectification strategy to mitigate the adverse effects of noisy data based on the identification results.
• Score: 76.02688769599686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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