An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization

TL;DR

This paper introduces CE-MVC, an adaptive multi-view clustering framework that uses conditional entropy to weigh views and a parameter-decoupled model to improve robustness against noisy data, leading to superior clustering results.

Contribution

The paper presents a novel framework combining adaptive weighting with a parameter-decoupled deep model for enhanced multi-view clustering performance.

Findings

CE-MVC outperforms existing methods in robustness and accuracy.

The framework effectively mitigates the impact of noisy views.

Extensive experiments validate the superiority of CE-MVC.

Abstract

Multi-view clustering (MVC) has emerged as a powerful technique for extracting valuable insights from data characterized by multiple perspectives or modalities. Despite significant advancements, existing MVC methods struggle with effectively quantifying the consistency and complementarity among views, and are particularly susceptible to the adverse effects of noisy views, known as the Noisy-View Drawback (NVD). To address these challenges, we propose CE-MVC, a novel framework that integrates an adaptive weighting algorithm with a parameter-decoupled deep model. Leveraging the concept of conditional entropy and normalized mutual information, CE-MVC quantitatively assesses and weights the informative contribution of each view, facilitating the construction of robust unified representations. The parameter-decoupled design enables independent processing of each view, effectively mitigating the influence of noise and enhancing overall clustering performance. Extensive experiments demonstrate that CE-MVC outperforms existing approaches, offering a more resilient and accurate solution for multi-view clustering tasks.