Identifiability, Separability, and Uniqueness of Linear ICA Models
Jenny Eriksson, V. Koivunen · IEEE Signal Processing Letters · 2004
In this letter, we give the conditions for identifiability, separability and uniqueness of linear real valued independent component analysis (ICA) models. A theorem is formulated and a proof is provided for each of the above concepts. These results extend the conditions for solving ICA problems, originally established by Comon , to wider class of mixing models and source distributions. Examples clarifying the above concepts are presented as well.