通过条件熵优化，自适应加权多视角数据，提升聚类鲁棒性。
An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization
- 用条件熵量化各视角贡献，动态调整权重
- 在多个数据集上准确率提升3%-7%，显著抗噪声
- 适合有噪声或异质多源数据的聚类任务
多视角聚类（MVC）已成为从多模态数据中提取有价值信息的强大技术。尽管取得显著进展，现有方法仍难以有效量化视角间的一致性与互补性，且易受噪声视角影响，即‘噪声视角缺陷’（NVD）。为此，我们提出CE-MVC框架，融合自适应加权算法与参数解耦深度模型。基于条件熵与归一化互信息，该框架定量评估并加权各视角的信息贡献，构建稳健的统一表征。参数解耦设计使各视角独立处理，有效缓解噪声干扰，提升整体聚类性能。大量实验表明，CE-MVC优于现有方法，在多个数据集上平均提升3%-7%的聚类准确率，提供更鲁棒、精准的多视角聚类解决方案。
原文摘要 · Abstract (English)
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.
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