Online Learning for Latent Dirichlet Allocation

author: Matt Hoffman, Adobe Systems Incorporated
published: March 25, 2011,   recorded: December 2010,   views: 1490
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Description

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.

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Download slides icon Download slides: nips2010_hoffman_oll_01.pdf (123.7 KB)

Download article icon Download article: nips2010_1291.pdf (658.2 KB)


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