Product Centred Dirichlet Processes for Bayesian Multiview Clustering.

ABSTRACT: While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistically dependent clusterings in a common set of entities for different data types. For example, clustering patients into subgroups with subgroup membership varying according to the domain of the patient variables. A challenge is how to model the across-view dependence between the partitions of patients into subgroups. The complexities of the partition space make standard methods to model dependence, such as correlation, infeasible. In this article, we propose CLustering with Independence Centring (CLIC), a clustering prior that uses a single parameter to explicitly model dependence between clusterings across views.

SUBMITTER: Dombowsky A 

PROVIDER: S-EPMC12392789 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature