Spectral signal unmixing with interior-point nonnegative matrix factorization

Abstract

Nonnegative Matrix Factorization (NMF) is an unsupervised learning method that has been already applied to many applications of spectral signal unmixing. However, its efficiency in some applications strongly depends on optimization algorithms used for estimating the underlying nonnegatively constrained subproblems. In this paper, we attempt to tackle the optimization tasks with the inexact Interior-Point (IP) algorithm that has been successfully applied to image deblurring [S. Bonettini, T. Serafini, 2009]. The experiments demonstrate that the proposed NMF algorithm considerably outperforms the well-known NMF algorithms for blind unmixing of the Raman spectra. © 2012 Springer-Verlag.