Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
High-order contrasts for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
An Improved Cumulant Based Method for Independent Component Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Estimation of entropy and mutual information
Neural Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Principal independent component analysis
IEEE Transactions on Neural Networks
Estimating Squared-Loss Mutual Information for Independent Component Analysis
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
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A new gradient technique is introduced for linear independent component analysis (ICA) based on the Edgeworth expansion of mutual information, for which the algorithm operates sequentially using fixed-point iterations. In order to address the adverse effect of outliers, a robust version of the Edgeworth expansion is adopted, in terms of robust cumulants, and robust derivatives of the Hermite polynomials are used. Also, a new constrained version of ICA is introduced, based on goal programming of mutual information objectives, which is applied to the extraction of the antepartum fetal electrocardiogram from multielectrode cutaneous recordings on the mother's thorax and abdomen.