Joint generative and alignment adversarial learning for robust incomplete multi-view clustering.

Abstract

Incomplete multi-view clustering (IMVC) has become an area of increasing focus due to the frequent occurrence of missing views in real-world multi-view datasets. Traditional methods often address this by attempting to recover the missing views before clustering. However, these methods face two main limitations: (1) inadequate modeling of cross-view consistency, which weakens the relationships between views, especially with a high missing rate, and (2) limited capacity to generate realistic and diverse missing views, leading to suboptimal clustering results. To tackle these issues, we propose a novel framework, Joint Generative Adversarial Network and Alignment Adversarial (JGA-IMVC). Our framework leverages adversarial learning to simultaneously generate missing views and enforce consistency alignment across views, ensuring effective reconstruction of incomplete data while preserving underlying structural relationships. Extensive experiments on benchmark datasets with varying missing rates demonstrate that JGA-IMVC consistently outperforms current state-of-the-art methods. The model achieves improvements of 3 % to 5 % in key clustering metrics such as Accuracy, Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). JGA-IMVC excels under high missing conditions, confirming its robustness and generalization capabilities, providing a practical solution for incomplete multi-view clustering scenarios.