Entropy regularization in probabilistic clustering

Beatrice Franzolini () and Giovanni Rebaudo
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Beatrice Franzolini: Bocconi University

Statistical Methods & Applications, 2024, vol. 33, issue 1, No 2, 37-60

Abstract: Abstract Bayesian nonparametric mixture models are widely used to cluster observations. However, one major drawback of the approach is that the estimated partition often presents unbalanced clusters’ frequencies with only a few dominating clusters and a large number of sparsely-populated ones. This feature translates into results that are often uninterpretable unless we accept to ignore a relevant number of observations and clusters. Interpreting the posterior distribution as penalized likelihood, we show how the unbalance can be explained as a direct consequence of the cost functions involved in estimating the partition. In light of our findings, we propose a novel Bayesian estimator of the clustering configuration. The proposed estimator is equivalent to a post-processing procedure that reduces the number of sparsely-populated clusters and enhances interpretability. The procedure takes the form of entropy-regularization of the Bayesian estimate. While being computationally convenient with respect to alternative strategies, it is also theoretically justified as a correction to the Bayesian loss function used for point estimation and, as such, can be applied to any posterior distribution of clusters, regardless of the specific model used.

Keywords: Dirichlet process; Loss functions; Mixture models; Unbalanced clusters; Random partition (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-023-00716-y

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