Conditional Partial Exchangeability: A Probabilistic Framework for Multi-View Clustering

• 成果类型: Article; Early Access
• 署名作者: Franzolini, Beatrice; De Iorio, Maria; Eriksson, Johan
• 署名单位: Bocconi University; National University of Singapore; Agency for Science Technology & Research (A*STAR)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2609381
• 发表日期: 2026-03-23
• 关键词: Bayesian Nonparametrics; dynamic clustering; Hierarchical processes; Partial exchangeability; random partitions; unsupervised learning; Dirichlet process; sampling methods; mixture-models; inference; regression; profiles; number; priors

摘要

Standard clustering techniques assume a common clustering configuration for all features in a dataset. However, when dealing with multi-view or longitudinal data, the clusters' number, frequencies, and shapes may need to vary across features to accurately capture dependence structures and heterogeneity. In this setting, classical model-based clustering fails to account for within-subject dependence across domains. We introduce conditional partial exchangeability, a novel probabilistic paradigm for dependent random partitions of the same objects across distinct domains. Additionally, we study a wide class of Bayesian clustering models based on conditional partial exchangeability, which allows for flexible dependent clustering of individuals across features, capturing the specific contribution of each feature and the within-subject dependence while ensuring computational feasibility. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.