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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... (Update)
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BibTeX entry: (Update)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. In: NIPS*14. (2002) to appear. http://citeseer.ist.psu.edu/blei03latent.html More
@misc{ blei02latent,
author = "D. Blei and A. Ng and M. Jordan",
title = "Latent Dirichlet allocation",
text = "Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation.",
journal = "Journal of machine Learning Research 3",
page = "993-1022",
year = "2003",
url = "citeseer.ist.psu.edu/blei03latent.html",
url = "http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf#search=%22Latent%20Dirichlet%20Allocation%22" }
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