Online inference of topics with latent dirichlet allocation

Kevin R. Canini, Lei Shi, Thomas L. Griffiths

Research output: Contribution to journalConference articlepeer-review

121 Scopus citations


Inference algorithms for topic models are typically designed to be run over an entire collection of documents after they have been observed. However, in many applications of these models, the collection grows over time, making it infeasible to run batch algorithms repeatedly. This problem can be addressed by using online algorithms, which update estimates of the topics as each document is observed. We introduce two related Rao-Blackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model incremental Gibbs samplers and particle filters and compare their runtime and performance to that of existing algorithms.

Original languageEnglish (US)
Pages (from-to)65-72
Number of pages8
JournalJournal of Machine Learning Research
StatePublished - 2009
Externally publishedYes
Event12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 - Clearwater, FL, United States
Duration: Apr 16 2009Apr 18 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


Dive into the research topics of 'Online inference of topics with latent dirichlet allocation'. Together they form a unique fingerprint.

Cite this