超越独立性：面向变分不完备多视图聚类的相关视图学习

中文摘要 AI 辅助

不完备多视图聚类（IMVC）旨在从部分视图存在缺失的数据中挖掘共享的聚类结构。尽管近期基于变分推理的无补全方法对缺失视图具有鲁棒性，但它们通常在后验聚合阶段依赖视图间的条件独立性假设，该假设无法捕捉多视图数据固有的结构化及潜在相关特性。本文提出一种变分框架，通过引入可学习的跨视图相关结构明确突破上述假设。具体而言，我们在聚合过程中利用后验估计误差的协方差结构，显式建模并学习视图间的相关性。为实现鲁棒高效的学习，相关矩阵通过归一化Cholesky分解进行参数化，确保正定性，使整个模型可通过统一的变分目标联合训练。在多个IMVC基准上开展的大量实验表明，我们的方法在各类缺失视图设置下均持续优于现有最优方法，且仅引入可忽略的可学习参数数量。这些结果凸显了自适应相关性建模在变分IMVC中的有效性，证明IMVC中突破独立性假设的必要性。代码可访问此https URL。

英文摘要

Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views. Although recent imputation-free methods based on variational inference demonstrate robustness to missing views, they commonly rely on a conditional independence assumption across views in the posterior aggregation stage, which fails to capture the inherently structured and potentially correlated nature of multi-view data. In this paper, we propose a variational framework that explicitly goes beyond this assumption by introducing a learnable cross-view correlation structure. Specifically, we explicitly model and learn correlations between views by utilizing the covariance structure of posterior estimation errors during aggregation. To facilitate robust and efficient learning, the correlation matrix is parameterized through a normalized Cholesky decomposition, ensuring positive definiteness and enabling the entire model to be trained jointly through a unified variational objective. Extensive experiments on multiple IMVC benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches across diverse missing-view settings while introducing only a negligible number of learnable parameters. These results highlight the effectiveness of adaptive correlation modeling in variational IMVC, demonstrating the need to go beyond the independence assumption in IMVC. The code is available at https://github.com/zmxu196/ACOVA.

发表机构

• School of Computer Science & Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
• Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, China（交通大数据与人工智能教育部重点实验室）
• Department of Data Science, BI Norwegian Business School（挪威商学院数据科学系）
• Department of Physics and Technology, UiT The Arctic University of Norway（挪威北极大学物理与技术系）
• Norwegian Computing Center（挪威计算中心）

机构由 AI 辅助整理，请以论文原文为准。