Memory Bounded Inference in Topic Models

Abstract

What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version of a variational EM algorithm that approximates inference of a topic model. The algorithm alternates two phases: "model building" and "model compression" in order to always satisfy a given memory constraint. The model building phase grows its internal representation (the number of topics) as more data arrives through Bayesian model selection. Compression is achieved by merging data-items in clumps and only caching their sufficient statistics. Empirically, the resulting algorithm is able to handle datasets that are orders of magnitude larger than the standard batch version.