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Accelerating collapsed variational bayesian inference for latent dirichlet allocation with nvidia CUDA compatible devices


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Title: Accelerating collapsed variational bayesian inference for latent dirichlet allocation with nvidia CUDA compatible devices
Authors: Masada, Tomonari / Hamada, Tsuyoshi / Shibata, Yuichiro / Oguri, Kiyoshi
Issue Date: 2009
Publisher: Springer Berlin
Citation: Lecture Notes in Computer Science, 5579, pp.491-500; 2009
Abstract: In this paper, we propose an acceleration of collapsed variational Bayesian (CVB) inference for latent Dirichlet allocation (LDA) by using Nvidia CUDA compatible devices. While LDA is an efficient Bayesian multi-topic document model, it requires complicated computations for parameter estimation in comparison with other simpler document models, e.g. probabilistic latent semantic indexing, etc. Therefore, we accelerate CVB inference, an efficient deterministic inference method for LDA, with Nvidia CUDA. In the evaluation experiments, we used a set of 50,000 documents and a set of 10,000 images. We could obtain inference results comparable to sequential CVB inference.
Description: Next-Generation Applied Intelligence: 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Tainan, Taiwan, June 24-27, 2009.
URI: http://hdl.handle.net/10069/22536
ISBN: 978-3-642-02567-9
ISSN: 03029743
DOI: 10.1007/978-3-642-02568-6_50
Rights: © 2009 Springer Berlin Heidelberg. / The original publication is available at www.springerlink.com
Type: Journal Article
Text Version: author
Appears in Collections:Articles in academic journal

Citable URI : http://hdl.handle.net/10069/22536

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