  In the internet era there has been an explosion in the amount of digital text
information available, leading to difficulties of scale for traditional
inference algorithms for topic models. Recent advances in stochastic
variational inference algorithms for latent Dirichlet allocation (LDA) have
made it feasible to learn topic models on large-scale corpora, but these
methods do not currently take full advantage of the collapsed representation of
the model. We propose a stochastic algorithm for collapsed variational Bayesian
inference for LDA, which is simpler and more efficient than the state of the
art method. We show connections between collapsed variational Bayesian
inference and MAP estimation for LDA, and leverage these connections to prove
convergence properties of the proposed algorithm. In experiments on large-scale
text corpora, the algorithm was found to converge faster and often to a better
solution than the previous method. Human-subject experiments also demonstrated
that the method can learn coherent topics in seconds on small corpora,
facilitating the use of topic models in interactive document analysis software.
