# Latent dirichlet allocation

**David M. Blei****Andrew Y. Ng****Michael I. Jordan**

CiteWeb id: 20030000007

CiteWeb score: 14926

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.

**Latent dirichlet allocation**" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Computer Science it is included to the Top 100. Additionally, the publicaiton "

**Latent dirichlet allocation**" is placed in the Top 100 among other scientific works published in 2003.

David M. Blei, Andrew Y. Ng, Michael I. Jordan,

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