DATA-PARALLEL PARAMETER ESTIMATION OF THE LATENT DIRICHLET ALLOCATION MODEL BY GREEDY GIBBS SAMPLING

摘要

A novel data-parallel algorithm is presented for topic modeling on a highly-parallel hardware architectures. The algorithm is a Markov-Chain Monte Carlo algorithm used to estimate the parameters of the LDA topic model. This algorithm is based on a highly parallel partially-collapsed Gibbs sampler, but replaces a stochastic step that draws from a distribution with an optimization step that computes the mean of the distribution directly and deterministically. This algorithm is correct, it is statistically performant, and it is faster than state-of-the art algorithms because it can exploit the massive amounts of parallelism by processing the algorithm on a highly-parallel architecture, such as a GPU. Furthermore, the partially-collapsed Gibbs sampler converges about as fast as the collapsed Gibbs sampler and identifies solutions that are as good, or even better, as the collapsed Gibbs sampler.