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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Massimo Bilancia & Michele Nanni & Fabio Manca & Gianvito Pio, 2023. "Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering," Computational Statistics, Springer, vol. 38(4), pages 2015-2051, December.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01350-8
    DOI: 10.1007/s00180-023-01350-8
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