Regularization, robustness and sparsity of probabilistic topic models

Regularization, robustness and sparsity of probabilistic topic models

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 p...

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Bibliographic Details
Main Authors: Konstantin Vyacheslavovich Vorontsov, Anna Alexandrovna Potapenko
Format: Article
Language:Russian
Published: Institute of Computer Science 2012-12-01
Series:Компьютерные исследования и моделирование
Subjects:
text analysis
topic modeling
probabilistic latent semantic analysis
EM-algorithm
latent Dirichlet allocation
Gibbs sampling
Bayesian regularization
perplexity
robusteness
Online Access:http://crm.ics.org.ru/uploads/crmissues/crm_2012_4/12403.pdf

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http://crm.ics.org.ru/uploads/crmissues/crm_2012_4/12403.pdf

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