Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation

Thomas Brouwer, Jes Frellsen, Pietro Liò

Research output: Contribution to conference - NOT published in proceeding or journalPaperResearchpeer-review

Abstract

We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively.
Original languageEnglish
Publication date9 Dec 2016
Publication statusPublished - 9 Dec 2016
EventNIPS 2016: Advances in Approximate Bayesian Inference Workshop - Room 112, Centre Convencions Internacional Barcelona, Barcelona, Spain
Duration: 9 Dec 20169 Dec 2016
http://approximateinference.org

Workshop

WorkshopNIPS 2016
LocationRoom 112, Centre Convencions Internacional Barcelona
Country/TerritorySpain
City Barcelona
Period09/12/201609/12/2016
Internet address

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