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.

翻译：多视图聚类（MVC）已成为从具有多视角或多模态特征的数据中提取有价值信息的有力技术。尽管取得了显著进展，但现有的MVC方法在有效量化视图间的一致性与互补性方面仍存在不足，且特别容易受到噪声视图（即“噪声视图缺陷”，NVD）不利影响的干扰。为应对这些挑战，我们提出CE-MVC——一种将自适应加权算法与参数解耦深度模型相结合的新型框架。通过利用条件熵与归一化互信息的概念，CE-MVC能够定量评估并加权每个视图的信息贡献度，从而促进构建鲁棒的统一表示。参数解耦设计使得各视图可独立处理，有效抑制噪声影响并提升整体聚类性能。大量实验表明，CE-MVC在多项指标上优于现有方法，为多视图聚类任务提供了更具鲁棒性和准确性的解决方案。