Non-negative matrix factorization with Gaussian process priors

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    We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors.
    We assume that the nonnegative factors in 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
    that 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.
    Original languageEnglish
    Article number361705
    JournalComputational Intelligence and Neuroscience
    Number of pages10
    Publication statusPublished - 2008

    Bibliographical note

    Copyright © 2008 Mikkel N. Schmidt and Hans Laurberg. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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