Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
This work addresses a domain-specific problem in nonnegative blind source separation, offering an incremental improvement over existing methods.
The authors tackled the problem of near-separable nonnegative matrix factorization by proposing the successive nonnegative projection algorithm (SNPA), which is more robust and applicable to a broader class of matrices than the existing successive projection algorithm (SPA), as demonstrated on synthetic and real-world hyperspectral data.
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.