提出自动识别并修复噪声数据的多视角聚类方法，提升真实场景下聚类鲁棒性。
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
- 将噪声识别转化为异常检测问题，用GMM实现自动定位噪声数据
- 设计混合修复策略与抗噪对比学习机制，显著降低噪声干扰
- 理论证明可剔除噪声信息，适合高噪声实际应用的开发者参考
近年来，深度多视角聚类方法利用强大的表征学习能力，通过有效整合多源异构视图信息，展现出可靠性能。然而，现有方法普遍依赖干净视图假设，而现实场景中噪声普遍存在，导致性能显著下降。为此，本文提出一种新型多视角聚类框架AIRMVC，实现噪声数据的自动识别与修复。具体而言，将噪声识别重构为基于GMM的异常检测问题；据此设计混合修复策略以缓解噪声影响；同时引入抗噪对比学习机制，生成鲁棒表征。此外，本文提供理论证明，表明该表征能有效剔除噪声信息，提升下游任务表现。在六个基准数据集上的大量实验表明，AIRMVC在噪声环境下优于当前最优算法。代码已开源：https://github.com/xihongyang1999/AIRMVC。
原文摘要 · Abstract (English)
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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