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

2025-05-27

AI Summary

To address the significant performance degradation of multi-view clustering under real-world noisy views, this paper proposes AIRMVC, an end-to-end robust framework that— for the first time—formulates noisy view identification as a Gaussian Mixture Model (GMM)-driven anomaly detection task. It introduces a hybrid correction strategy and a noise-robust contrastive learning mechanism, with theoretical analysis proving their effectiveness in filtering out noise-induced distortions. By unifying multi-view representation learning, self-supervised correction, and anomaly-aware fusion, AIRMVC achieves state-of-the-art performance across six benchmark datasets, improving average clustering accuracy by over 8% under high-noise conditions. The source code is publicly available.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Automatically identify and rectify noisy data in multi-view clustering
• Improve clustering performance in noisy real-world scenarios
• Develop robust contrastive mechanisms for reliable representation learning

Innovation

• GMM-based noisy data identification
• Hybrid rectification strategy for noise
• Noise-robust contrastive representation mechanism