An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization
This addresses the Noisy-View Drawback in multi-view clustering for data analysis applications, but it appears incremental as it builds on existing methods with specific enhancements.
They tackled the problem of noisy views in multi-view clustering by proposing CE-MVC, a framework that uses adaptive weighting and a parameter-decoupled deep model, resulting in improved clustering performance as demonstrated in experiments.
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.