.png){kind=icon}
.png){kind=icon}
Return
Conditional Partial Exchangeability: A Probabilistic Framework for Multi-View Clustering
.png){kind=icon}
PRE
AI
DOI：10.1080/01621459.2025.2609381.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian nonparametrics
Dynamic clustering
Hierarchical processes
Partial exchangeability
Random partitions
Unsupervised learning
Journal
J
IF:
3
Papers:
5.1K
Citations:
4.8W