Online Learning for Latent Dirichlet Allocation. Hoffman, M., D., Blei, D., M., & Bach, F.
Paper
Website abstract bibtex We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Al-location (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, includ-ing 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.
@article{
title = {Online Learning for Latent Dirichlet Allocation},
type = {article},
websites = {https://papers.nips.cc/paper/3902-online-learning-for-latent-dirichlet-allocation.pdf},
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created = {2018-02-05T16:53:51.835Z},
accessed = {2018-02-05},
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abstract = {We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Al-location (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, includ-ing 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.},
bibtype = {article},
author = {Hoffman, Matthew D and Blei, David M and Bach, Francis}
}