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arXiv · 1206.6425

Sparse Stochastic Inference for Latent Dirichlet allocation

David Mimno·Matt Hoffman·David Blei
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Abstract

We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.

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David Mimno, Matt Hoffman, David Blei. 2012-06-27. Sparse Stochastic Inference for Latent Dirichlet allocation. https://arxiv.org/abs/1206.6425

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