Scalable deep Poisson factor analysis for topic modeling

Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations

Abstract

A new framework for topic modeling is developed, based on deep graphical models, where interactions between topics are inferred through deep latent binary hierarchies. The proposed multi-layer model employs a deep sigmoid belief network or restricted Boltzmann machine, the bottom binary layer of which selects topics for use in a Poisson factor analysis model. Under this setting, topics live on the bottom layer of the model, while the deep specification serves as a flexible prior for revealing topic structure. Scalable inference algorithms are derived by applying Bayesian conditional density filtering algorithm, in addition to extending recently proposed work on stochastic gradient thermostats. Experimental results on several corpora show that the proposed approach readily handles very large collections of text documents, infers structured topic representations, and obtains superior test perplexities when compared with related models.
Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
PublisherInternational Machine Learning Society (IMLS)rasmussen@ptd.net
Pages1823-1832
Number of pages10
ISBN (Print)9781510810587
StatePublished - Jan 1 2015
Externally publishedYes

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