Stochastic variational inference for clustering short text data with finite mixtures of Dirichlet-Multinomial distributions
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DOI: 10.1007/s00362-025-01702-0
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- David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
- Franca Debole & Fabrizio Sebastiani, 2005. "An analysis of the relative hardness of Reuters‐21578 subsets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(6), pages 584-596, April.
- Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
- 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.
- H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
- repec:dau:papers:123456789/4648 is not listed on IDEAS
- Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
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Keywords
Dirichlet-Multinomial mixture model; Text categorization; Variational inference; Stochastic variational inference; Numerical optimization;All these keywords.
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