@CONFERENCE\{IMM2010-05937,
    author       = "M. N. Schmidt and M. M{\o}rup",
    title        = "Infinite non-negative matrix factorization",
    year         = "2010",
    month        = "aug",
    keywords     = "non-negative matrix factorization (NMF), nonparametric Bayes",
    booktitle    = "European Signal Processing Conference (EUSIPCO)",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    organization = "",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5937-full.html",
    abstract     = "We propose the infinite non-negative 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
 Metropolis-Hastings moves that admits cross-dimensional 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}."
}