Distributed Online Learning for Latent Dirichlet Allocation

A major obstacle in using Latent Dirichlet Allocation (LDA) is the amount of time it takes for inference, especially for a dataset that starts out large and expands quickly, such as a corpus of blog posts or online news articles. Recent developments in distributed inference algorithms for LDA, as well as minibatchbased online learning algorithms have offered partial solutions for problem. In this paper, we propose a distributed online learning algorithm for LDA for dealing with both aspects of this problem at once. We apply our learning algorithm to a corpus of Twitter conversations and show that it achieves the same model fit within a much shorter learning time.