Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
  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. 
Thomas Brouwer, Jes Frellsen, Pietro Lio'
cs.LG cs.AI cs.NA stat.ML