Regularization, robustness and sparsity of probabilistic topic models

Vorontsov K.V., Potapenko A.A.
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We propose a generalized probabilistic topic model of text corpora which can incorporate heuristics of Bayesian regularization, sampling, frequent parameters update, and robustness in any combinations. Wellknown models PLSA, LDA, CVB0, SWB, and many others can be considered as special cases of the proposed broad family of models. We propose the robust PLSA model and show that it is more sparse and performs better that regularized models like LDA.

Keywords: text analysis, topic modeling, probabilistic latent semantic analysis, EM-algorithm, latent Dirichlet allocation, Gibbs sampling, Bayesian regularization, perplexity, robusteness
Citation in English: Vorontsov K.V., Potapenko A.A. Regularization, robustness and sparsity of probabilistic topic models // Computer Research and Modeling, 2012, vol. 4, no. 4, pp. 693-706
Citation in English: Vorontsov K.V., Potapenko A.A. Regularization, robustness and sparsity of probabilistic topic models // Computer Research and Modeling, 2012, vol. 4, no. 4, pp. 693-706
DOI: 10.20537/2076-7633-2012-4-4-693-706
URL: http://crm-en.ics.org.ru/journal/article/1950/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.
According to Crossref, this article is cited by:
  • Maria Saburova, Archil Maysuradze. Knowledge Engineering and Semantic Web. / Communications in Computer and Information Science. — 2015. — V. 518. — P. 168. DOI: 10.1007/978-3-319-24543-0_13
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