RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908659591675904 |
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| author | Dong, Shihao Liu, Yue Zhou, Xiaotong Zheng, Yuhui Xu, Huiying Zhu, Xinzhong |
| author_facet | Dong, Shihao Liu, Yue Zhou, Xiaotong Zheng, Yuhui Xu, Huiying Zhu, Xinzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise Dong, Shihao Liu, Yue Zhou, Xiaotong Zheng, Yuhui Xu, Huiying Zhu, Xinzhong Machine Learning 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. |
| title | RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.13561 |