Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation
Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Analysing MEG-data by a combination of different neural networks
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
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According to the common underlying mathematical model for Independent Component Analysis (ICA), the fulfillment of a BSS for either a linear and scalar type of composition, a convolutive and linear type or a nonlinear type has different conditions. So far, several approaches have been developed in the last decades for stationary and non-stationary data. To identify key research priorities, the different origins of neural network approaches for BSS are briefly reviewed and divided by classes of specific theoretical and application features. A principal guideline for the design of reference data sets for the comparison of all the existing ICA methods by its individual strengths and weaknesses for performing BSS is developed.