Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient learning for over- and under-complete bases in ICA
Neural Computation
General approach to blind source separation
IEEE Transactions on Signal Processing
Iterative algorithms based on multistage criteria for multichannelblind deconvolution
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Beyond Comon's Identifiability Theorem for Independent Component Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
On the entropy minimization of a linear mixture of variables for source separation
Signal Processing - Special issue: Information theoretic signal processing
Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
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In this paper we address the problem of blind source extraction of a subset of "interesting" independent sources from a linear convolutive or instantaneous mixture. The interesting sources are those which are independent and, in a certain sense, are sparse and far away from Gaussianity. We show that in the low-noise limit and when none of the desired sources is Gaussian, the minimum entropy and cumulants based approaches can solve the problem. These criteria, with roots in Blind Deconvolution and in Projection Pursuit, will be proposed here for the simultaneous blind extraction of a group of independent sources. Then, we suggest simple algorithms which, working on the Stiefel manifold perform maximization of the proposed contrast functions.