Fast local learning regularized nonnegative matrix factorization

Abstract

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. In this paper, we present a fast algorithm to solve local learning regularized nonnegative matrix factorization. We consider not only the local learning, but also its convergence speed. Experiments on many benchmark data sets demonstrate that the proposed method outperforms the local learning regularized NMF in convergence speed. © 2012 Springer-Verlag GmbH.