Paper: Particle Filter Rejuvenation and Latent Dirichlet Allocation

ACL ID P14-2073
Title Particle Filter Rejuvenation and Latent Dirichlet Allocation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Previous research has established sev- eral methods of online learning for la- tent Dirichlet allocation (LDA). How- ever, streaming learning for LDA? allowing only one pass over the data and constant storage complexity?is not as well explored. We use reservoir sam- pling 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 im- plementation performs just as well, and that the quality of the initialization dom- inates other factors of performance.