Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
The Minimum Entropy and Cumulants Based Contrast Functions for Blind Source Extraction
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
An iterative inversion approach to blind source separation
IEEE Transactions on Neural Networks
Journal of VLSI Signal Processing Systems
A semiparametric approach to source separation using independent component analysis
Computational Statistics & Data Analysis
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In this paper, Comon's conventional identifiability theorem for Independent Component Analysis (ICA) is extended to the case of mixtures where several gaussian sources are present. We show, in an original and constructive proof, that using the conventional mutual information minimization framework, the separation of all the non-gaussian sources is always achievable (up to scaling factors and permutations). In particular, we prove that a suitably designed optimization framework is capable of seamlessly handling both the case of one single gaussian source being present in the mixture (separation of all sources achievable), as well as the case of multiple gaussian signals being mixed together with non-gaussian signals (only the non-gaussian sources can be extracted).