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Latent Dirichlet Allocation

热3 xnature 添加于 2010-3-29 23:30 | 1924 次阅读 | 1 个评论

作者
Blei DM, Ng AY, Jordan MI, Lafferty J
摘要
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.
详细资料
- 文献种类:期刊
- 期刊名称: Journal of Machine Learning Research
- 期刊缩写: Journal of Machine Learning Research
- 期卷页: 2003年第3卷第4-5期 993-1022页
- ISBN: 1532-4435
标签

language_model
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