2003 LatentDirichletAllocation

• (Blei, Ng & Jordan, 2003) ⇒ David M. Blei, Andrew Y. Ng, and Michael I. Jordan. (2003). “Latent Dirichlet Allocation.” In: The Journal of Machine Learning Research, 3. doi:10.1162/jmlr.2003.3.4-5.993

Subject Headings: Latent Dirichlet Allocation, TF-IDF, pLSI.

Notes

• This is the journal paper of (Blei, Ng & Jordan, 2001) presented at NIPS 2001.

Cited By

• ~2,951 http://scholar.google.com/scholar?q=%22Latent+Dirichlet+Allocation%22+2003
• ~ 782 http://portal.acm.org/citation.cfm?id=944937&preflayout=flat#citedby

2004

• (Griffiths & Steyvers, 2004) ⇒ Thomas L. Griffiths, and Mark Steyvers. (2004). “Finding Scientific Topics.” In: Proceedings of the National Academy of Sciences (PNAS), 101(Suppl. 1). doi:10.1073/pnas.0307752101

Quotes

Abstract

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 LatentDirichletAllocation	Andrew Y. Ng Michael I. Jordan David M. Blei			Latent Dirichlet Allocation		The Journal of Machine Learning Research	http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf	10.1162/jmlr.2003.3.4-5.993		2003