Infinite non-negative matrix factorization

Mikkel N. Schmidt, Morten Mørup

AbstractWe 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.
Keywordsnon-negative matrix factorization (NMF), nonparametric Bayes
TypeConference paper [With referee]
ConferenceEuropean Signal Processing Conference (EUSIPCO)
Year2010    Month August
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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