Adaptive weights for NMF with additional priors

Nonnegative matrix factorization (NMF) has become a very popular method in various signal processing applications. Supporting NMF with additional cost functions, so called priors, is very helpful to adapt the factorization to specific tasks. Additional priors are usually multiplied by fixed weights to adjust the influence of the prior. The question how to adapt these weights to the needs of specific factorization scenarios is yet unsolved. In this paper, we present a method to adjust the weights iteratively throughout the NMF process. We evaluate our method in an audio source separation environment and show, that it is more robust than the recently used method with fixed weights and that it leads to better separation results.