Dirichlet mixture allocation

Jen-Tzung Chien, Chao Hsi Lee, Zheng Hua Tan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The topic model based on latent Dirichlet allocation relies on the prior statistics of topic proportionals for multinomial words. The words in a document are modeled as a random mixture of latent topics which are drawn from a single Dirichlet prior. However, a single Dirichlet distribution may not sufficiently characterize the variations of topic proportionals estimated from the heterogeneous documents. To deal with this concern, we present a Dirichlet mixture allocation (DMA) model which learns latent topics and their proportionals for topic and document clustering by using the prior based on a Dirichlet mixture model. Multiple Dirichlets pave a way to capture the structure of latent variables in learning representation from real-world documents covering a variety of topics. This paper builds a new latent variable model and develops a variational Bayesian inference procedure to learn model parameters consisting of mixture weights, Dirichlet parameters and word multinomials. Experiments on document representation show the merit of the proposed structural learning by increasing the number of Dirichlets in a DMA topic model.

Original languageEnglish
Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditorsKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
PublisherIEEE Computer Society
ISBN (Electronic)9781509007462
DOIs
StatePublished - 8 Nov 2016
Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
Duration: 13 Sep 201616 Sep 2016

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2016-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
CountryItaly
CityVietri sul Mare, Salerno
Period13/09/1616/09/16

Keywords

  • Bayesian learning
  • Dirichlet mixture model
  • structural learning
  • topic model

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