Distributing the Stochastic Gradient Sampler for Large-Scale LDA

Abstract

Learning large-scale Latent Dirichlet Allocation (LDA) models is beneficial for many applications that involve large collections of documents.Recent work has been focusing on developing distributed algorithms in the batch setting, while leaving stochastic methods behind, which can effectively explore statistical redundancy in big data and thereby are complementary to distributed computing.The distributed stochastic gradient Langevin dynamics (DSGLD) represents one attempt to combine stochastic sampling and distributed computing, but it suffers from drawbacks such as excessive communications and sensitivity to partitioning of datasets across nodes. DSGLD is typically limited to learn small models that have about 103 topics and $10^3$ vocabulary size.