Latent Dirichlet allocation
Latent Dirichlet allocation
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DOI：
10.1162/jmlr.2003.3.4-5.993
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发表时间：
2003-05-15
影响因子：
6
通讯作者：
Jordan, MI
中科院分区：
文献类型：
--
作者：
Blei, DM;Ng, AY;Jordan, MI
我们描述了潜在的狄利克雷分配（LDA），一个生成的概率模型，如文本语料库的离散数据的集合。LDA是一个三级层次贝叶斯模型，其中集合中的每个项目都被建模为一组底层主题上的有限混合。每个主题，反过来，建模为一个无限的混合在一组潜在的主题概率。在文本建模的上下文中，主题概率提供了文档的显式表示。我们提出了有效的近似推理技术的基础上变分方法和EM算法的经验贝叶斯参数估计。我们报告的结果，在文档建模，文本分类，和协同过滤，比较一元模型和概率LSI模型的混合物。
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.