Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908381629906944 |
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| author | Yang, Xihong Wang, Siwei Wang, Fangdi Jin, Jiaqi Liu, Suyuan Liu, Yue Zhu, En Liu, Xinwang Jin, Yueming |
| author_facet | Yang, Xihong Wang, Siwei Wang, Fangdi Jin, Jiaqi Liu, Suyuan Liu, Yue Zhu, En Liu, Xinwang Jin, Yueming |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_21387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Yang, Xihong Wang, Siwei Wang, Fangdi Jin, Jiaqi Liu, Suyuan Liu, Yue Zhu, En Liu, Xinwang Jin, Yueming Computer Vision and Pattern Recognition 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. |
| title | Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.21387 |