{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T02:40:13Z","timestamp":1778640013112,"version":"3.51.4"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,16]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Model-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\u00a0al. (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.<\/jats:p>","DOI":"10.1093\/biomet\/asae059","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T14:50:06Z","timestamp":1730731806000},"source":"Crossref","is-referenced-by-count":7,"title":["Bayesian clustering of high-dimensional data via latent repulsive mixtures"],"prefix":"10.1093","volume":"112","author":[{"given":"L","family":"Ghilotti","sequence":"first","affiliation":[{"name":"Department of Economics, Management and Statistics, University of Milano-Bicocca , Via Bicocca degli Arcimboldi 8 , 20126 Milan,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M","family":"Beraha","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Politecnico di Milano , Piazza Leonardo da Vinci 32 , 20133 Milan,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A","family":"Guglielmi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Politecnico di Milano , Piazza Leonardo da Vinci 32 , 20133 Milan,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"2025032019050536300_asae059-B1","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1093\/biomet\/asac051","article-title":"Clustering consistency with Dirichlet process mixtures","volume":"110","author":"Ascolani","year":"2023","journal-title":"Biometrika"},{"key":"2025032019050536300_asae059-B2","author":"Baccelli","year":"2020"},{"key":"2025032019050536300_asae059-B3","first-page":"3393","volume-title":"Proc. 28th Int. 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