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Online Learning for Latent Dirichlet Allocation
Matthew D. Hoffman · David Blei · Francis Bach

Wed Dec 08 12:05 PM -- 12:10 PM (PST) @ Regency Ballroom

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.

Author Information

Matthew D. Hoffman (Google)
David Blei (Columbia University)
Francis Bach (INRIA - Ecole Normale Superieure)

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