Non-negative matrix factorization with Gaussian process priors

Mikkel N. Schmidt, Hans Laurberg

AbstractWe present a general method for including prior knowledge in a non-negative matrix factorization based on Gaussian process priors. We assume that the non-negative parameters of the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance function. This allows us to find NMF decompositions which agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries. The method is demonstrated with an example from chemical shift brain imaging.
KeywordsNon-negative matrix factorization, NMF, Gaussian process, Chemical shift brain imaging
TypeJournal paper [With referee]
JournalComputational Intelligence and Neuroscience
EditorsAndrzej Cichocki, Morten Mørup, Paris Smaragdis, Wenwu Wang and Rafal Zdunek
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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