Bayesian clustering of high-dimensional data via latent repulsive mixtures

L Ghilotti, M Beraha and A Guglielmi

Biometrika, 2025, vol. 112, issue 2, 551-8

Abstract: SummaryModel-based clustering of moderate- or large-dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mixture model for a set of latent scores, which are then linked to the observations via a Gaussian latent factor model. This approach was recently investigated by Chandra et al. (2023). The authors used a factor-analytic representation and assumed a mixture model for the latent factors. However, performance can deteriorate in the presence of model misspecification. Assuming a repulsive point process prior for the component-specific means of the mixture for the latent scores is shown to yield a more robust model that outperforms the standard mixture model for the latent factors in several simulated scenarios. The repulsive point process must be anisotropic to favour well-separated clusters of data, and its density should be tractable for efficient posterior inference. We address these issues by proposing a general construction for anisotropic determinantal point processes. We illustrate our model in simulations, as well as a plant species co-occurrence dataset.

Keywords: Anisotropic point process; Determinantal point process; Gaussian factor model; Markov chain Monte Carlo; Model-based clustering (search for similar items in EconPapers)
Date: 2025
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