Abstract
The use of independent component analysis (ICA) methods for blind source separation of signals and images has been demonstrated in many applications and publications. While many ICA based algorithms for source separation exist, few impose physical constraints on the recovered independent components and the mixing matrix. Of particular interest is the non-negativity of the recovered independent components and the recovered mixing matrix. Such constraints are important for example when trying to do subpixel demixing on hyperspectral images. In this article, we propose a constrained non-negative maximum-likelihood ICA (CNML-ICA) algorithm that tackles the limitations of some existing non-negative ICA methods.
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