Nonnegative Matrix Factorization with Gaussian Process Priors

Schmidt, Mikkel N.; Laurberg, Hans
January 2008
Computational Intelligence & Neuroscience;2008 Supplement, Special section p1
Academic Journal
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.


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