  We propose an algorithm for the non-negative factorization of an occurrence
tensor built from heterogeneous networks. We use l0 norm to model sparse errors
over discrete values (occurrences), and use decomposed factors to model the
embedded groups of nodes. An efficient splitting method is developed to
optimize the nonconvex and nonsmooth objective. We study both synthetic
problems and a new dataset built from financial documents, resMBS.
