Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering

Massimo Bilancia (), Michele Nanni (), Fabio Manca () and Gianvito Pio ()
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Massimo Bilancia: University of Bari Aldo Moro, Policlinic University Hospital
Michele Nanni: EY Business and Technology Solution
Fabio Manca: University of Bari Aldo Moro, Palazzo Chiaia - Napolitano
Gianvito Pio: University of Bari Aldo Moro

Computational Statistics, 2023, vol. 38, issue 4, No 19, 2015-2051

Abstract: Abstract In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through variational inference. We compute the explicit expression of the variational objective function for our hierarchical model under a mean-field approximation. We then derive the update equations of a suitable algorithm based on coordinate ascent to find local maxima of the variational target, and estimate the model parameters through the optimized variational hyperparameters. The advantages of variational algorithms over traditional Markov Chain Monte Carlo methods based on iterative posterior sampling are also discussed in detail.

Keywords: Text clustering; Finite mixture models; Dirichlet-multinomial distribution; Bayesian hierarchical modelling; Variational inference (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-023-01350-8

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