2001 LatentDirichletAllocation

• (Blei et al., 2001) ⇒ David M. Blei, Andrew Y. Ng, and Michael I. Jordan. (2001). “Latent Dirichlet Allocation.” In: Advances in Neural Information Processing Systems (NIPS-2001)

Subject Headings: Latent Dirichlet Allocation.

Notes

• Extended as a journal paper (Blei, Ng & Jordan, 2003).

Cited By

• http://scholar.google.com/scholar?q=%222001%22+Latent+Dirichlet+Allocation

Quotes

Abstract

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 LatentDirichletAllocation	Andrew Y. Ng Michael I. Jordan David M. Blei			Latent Dirichlet Allocation						2001