Sparse stochastic inference for latent Dirichlet allocation
Author(s): Mimno, David; Hoffman, Matthew D; Blei, David M
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http://arks.princeton.edu/ark:/88435/pr1352g| 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. |
| Publication Date: | Jun-2012 |
| Citation: | Mimno, David, Matthew D. Hoffman, David M. Blei. "Sparse stochastic inference for latent Dirichlet allocation." ICML'12: Proceedings of the 29th International Conference on International Conference on Machine Learning: pp. 1515–1522. |
| Pages: | 1515–1522 |
| Type of Material: | Conference Article |
| Series/Report no.: | ICML'12; |
| Journal/Proceeding Title: | ICML'12: Proceedings of the 29th International Conference on Machine Learning |
| Version: | Final published version. Article is made available in OAR by the publisher's permission or policy. |
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