Infinite nonnegative matrix factorization 
Mikkel N. Schmidt, Morten Mørup

Abstract  We propose the infinite nonnegative matrix factorization
(INMF) which assumes a potentially unbounded number of
components in the Bayesian NMF model. We devise an
inference scheme based on Gibbs sampling in conjunction with
MetropolisHastings moves that admits crossdimensional exploration
of the posterior density. The approach can effectively establish the
model order for NMF at a less computational cost than
existing approaches such as thermodynamic integration and existing
reversible jump Markov chain Monte Carlo sampling schemes. On
synthetic and real data we demonstrate the success of
INMF. 
Keywords  nonnegative matrix factorization (NMF), nonparametric Bayes 
Type  Conference paper [With referee] 
Conference  European Signal Processing Conference (EUSIPCO) 
Year  2010 Month August 
Publisher  Informatics and Mathematical Modelling, Technical University of Denmark, DTU 
Address  Richard Petersens Plads, Building 321, DK2800 Kgs. Lyngby 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 