Abstract
Finding low rank nonnegative decomposition of multivariate data has many important applications in signal processing. A standard method is the nonnegative matrix factorization (NMF). In recent years, many algorithm have been proposed for NMF. However, an important problem that has not received as much attention is the selection of the rank of NMF. In this paper we develop a method for selecting the rank of NMF based on the Stein's unbiased risk estimator (SURE). In simulations we compare the method against crossvalidation. In addition we apply the method for selecting the rank of NMF for high dimensional hyperspectral data.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
