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A class of neural networks for independent component analysis
1997
IEEE Transactions on Neural Networks
Independent component analysis (ICA) is a recently developed, useful extension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In this application only the source signals which correspond to the coefficients of the ICA expansion are of interest. In this paper, we propose neural structures related to multilayer feedforward networks for performing complete ICA. The basic ICA network consists
doi:10.1109/72.572090
pmid:18255654
fatcat:7ycqgkg5yvdhflruafe75wfaay