A class of neural networks for independent component analysis

J. Karhunen, E. Oja, L. Wang, R. Vigario, J. Joutsensalo
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
more » ... of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. We consider learning algorithms for each layer, and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved. The proposed class of networks yields good results in test examples with both artificial and real-world data. Index Terms-Blind source separation, independent component analysis, neural networks, principal component analysis, signal processing, unsupervised learning. Juha Karhunen (M'90) received the Doctor of Technology degree from the
doi:10.1109/72.572090 pmid:18255654 fatcat:7ycqgkg5yvdhflruafe75wfaay