@ARTICLE\{IMM2008-05528,
    author       = "M. N. Schmidt and H. Laurberg",
    title        = "Non-negative matrix factorization with Gaussian process priors",
    year         = "2008",
    keywords     = "Non-negative matrix factorization, {NMF,} Gaussian process, Chemical shift brain imaging",
    journal      = "Computational Intelligence and Neuroscience",
    volume       = "",
    editor       = "Andrzej Cichocki, Morten M{\o}rup, Paris Smaragdis, Wenwu Wang and Rafal Zdunek",
    number       = "",
    publisher    = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5528-full.html",
    abstract     = "We 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."
}