Latent Modularity in Multi-View Data

TL;DR

This paper introduces a Bayesian clustering model for multi-view data that accounts for individuals belonging to different clusters across views, providing a flexible framework for heterogeneous data integration.

Contribution

The paper proposes a novel Bayesian model with latent modularity allowing view-specific clustering, along with derivations of marginal priors and MCMC algorithms for inference.

Findings

Model effectively captures heterogeneity across views.

Simulation and case study demonstrate the model's practical utility.

Provides insights into prior structure and clustering behavior.

Abstract

In this article, we consider the problem of clustering multi-view data, that is, information associated to individuals that form heterogeneous data sources (the views). We adopt a Bayesian model and in the prior structure we assume that each individual belongs to a baseline cluster and conditionally allow each individual in each view to potentially belong to different clusters than the baseline. We call such a structure ''latent modularity''. Then for each cluster, in each view we have a specific statistical model with an associated prior. We derive expressions for the marginal priors on the view-specific cluster labels and the associated partitions, giving several insights into our chosen prior structure. Using simple Markov chain Monte Carlo algorithms, we consider our model in a simulation study, along with a more detailed case study that requires several modeling innovations.