Particle filter rejuvenation and latent Dirichlet allocation

Abstract

Previous research has established several methods of online learning for latent Dirichlet allocation (LDA). However, streaming learning for LDA - allowing only one pass over the data and constant storage complexity - is not as well explored. We use reservoir sampling to reduce the storage complexity of a previously-studied online algorithm, namely the particle filter, to constant. We then show that a simpler particle filter implementation performs just as well, and that the quality of the initialization dominates other factors of performance. © 2014 Association for Computational Linguistics.