CIS Publication Spotlight [Publication Spotlight]
Digital Object Identifi er: 10.1109/ TNN.2011.2109396 “Online blind source separation (BSS) can overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, existing online BSS methods are mainly used to separate independent or uncorrelated sources. In this paper, an incremental nonnegative matrix factorization (NMF) with volume constraint is derived and utilized for solving online BSS, which can be used to separate correlative sources. The volume constraint to the mixing matrix enhances the identifi ability of the sources, while the incremental learning mode reduces the computational cost. This new method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources.”