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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