Latent dirichlet allocation
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TLDR
This work proposes 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, and Hofmann's aspect model.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 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.read more
Citations
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References
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Estimating a Dirichlet Distribution
TL;DR: In this article, the Dirichlet distribution and its compound variant, Dirichletsmultinomial, are two of the most basic models for proportional data, such as the mix of vocabulary words in a text document, and the maximum likelihood estimate of these distributions is not available in closed-form.
Proceedings Article
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
TL;DR: It is shown that secondary content information can often be used to overcome sparsity and appropriate mixture models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN).
Journal ArticleDOI
Approximate Bayesian Inference in Conditionally Independent Hierarchical Models (Parametric Empirical Bayes Models)
Robert E. Kass,Duane Steffey +1 more
TL;DR: In this paper, conditionally independent hierarchical models of the kind used in parametric empirical Bayes (PEB) methodology are considered, where the observation vectors Yi for units i = 1, …, k are independently distributed with densities p(yi | θi, λ).
Proceedings Article
Expectation-propagation for the generative aspect model
Tom Minka,John Lafferty +1 more
TL;DR: This paper showed that the simple variational methods of Blei et al. (2001) can lead to inaccurate inferences and biased learning for the generative aspect model and developed an alternative approach that leads to higher accuracy at comparable cost.