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

TL;DR

This paper introduces AIRMVC, a deep multi-view clustering framework that automatically identifies and rectifies noisy data using anomaly detection, hybrid rectification, and noise-robust contrastive learning, significantly improving robustness in noisy real-world scenarios.

Contribution

The paper proposes a novel multi-view clustering method that automatically detects and corrects noisy data, with a theoretical proof of noise-discarding representations, enhancing robustness in noisy environments.

Findings

Outperforms state-of-the-art algorithms on six benchmark datasets.

Effectively identifies and rectifies noisy data in multi-view clustering.

Provides theoretical proof of noise-robust representation learning.

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.