Latent Modularity in Multi-View Data - Cremaschi, Andrea; De Iorio, Maria; Page, Garritt; Jasra, Ajay (My University, 2025)
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.
Keywords: 12 Matemáticas::1203 Ciencia de los ordenadores; ODS 9 - Industria, innovación e infraestructura; Multi-View Data; Latent Modularity; Clustering; Partitioning; Markov chain Monte Carlo