  Non-negative matrix factorization (NMF) is a technique for finding latent
representations of data. The method has been applied to corpora to construct
topic models. However, NMF has likelihood assumptions which are often violated
by real document corpora. We present a double parametric bootstrap test for
evaluating the fit of an NMF-based topic model based on the duality of the KL
divergence and Poisson maximum likelihood estimation. The test correctly
identifies whether a topic model based on an NMF approach yields reliable
results in simulated and real data.
