Emergent Mind
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
(1610.08127)
Published Oct 26, 2016
in
cs.LG
,
cs.AI
,
cs.NA
,
and
stat.ML
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.
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