document: Fast collapsed gibbs sampling for latent dirichlet allocation In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation ( LDA ) model . Our new method results in significant speedups on real world text corpora . Conventional Gibbs sampling schemes for LDA require O ( K ) operations per sample where K is the number of topics in the model . Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample . On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA . No approximations are necessary , and we show that our fast sampling scheme produces exactly the same results as the standard ( but slower ) sampling scheme . Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes . For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA , our speedup of 5.7 can save 5 CPU months of computation .
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