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Streaming Gibbs Sampling for LDA Model

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arxiv 1601.01142 v1 pith:6PYV6X5M submitted 2016-01-06 cs.LG stat.ML

Yang Gao , Jianfei Chen , Jun Zhu This is my paper · ORCID
classification cs.LGstat.ML
keywords gibbsmuchonlinesamplingstreamingperplexityattemptsbatch
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Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an online extension of the collapsed Gibbs sampling (CGS). Our empirical study shows that SGS can reach similar perplexity as CGS, much better than SVB. Our distributed version of SGS, DSGS, is much more scalable than SVB mainly because the updates' communication complexity is small.

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  1. MLego: Interactive and Scalable Topic Exploration Through Model Reuse

    cs.DB 2025-08 conditional novelty 6.0 of 10

    MLego reuses and merges materialized LDA models to answer ad-hoc topic queries quickly, using hierarchical plan search and batch reordering to keep the cost low.

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