  In this paper, we propose a new fast and robust recursive algorithm for
near-separable nonnegative matrix factorization, a particular nonnegative blind
source separation problem. This algorithm, which we refer to as the successive
nonnegative projection algorithm (SNPA), is closely related to the popular
successive projection algorithm (SPA), but takes advantage of the nonnegativity
constraint in the decomposition. We prove that SNPA is more robust than SPA and
can be applied to a broader class of nonnegative matrices. This is illustrated
on some synthetic data sets, and on a real-world hyperspectral image.
