Nonnegative Matrix Factorization with Gaussian Process Priors
Figure 1
Toy example
data matrix (upper left), underlying noise-free nonnegative data (upper right),
and estimates using the four methods described in the text. The data has a
fairly large amount of noise, and the underlying nonnegative factors are smooth
in both directions. The LS-NMF and CNMF
decompositions are nonsmooth since these methods are
not model of correlations in the factors. The GPP-NMF, which uses a smooth
prior, finds a smooth solution. When using the correct prior, the soulution is
very close to the true underlying data.