Abstract

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 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.

Keywords

Independent component analysisPrincipal component analysisBlind signal separationArtificial neural networkComputer sciencePattern recognition (psychology)Artificial intelligenceBasis (linear algebra)Feedforward neural networkSource separationNonlinear systemAlgorithmMathematicsChannel (broadcasting)Telecommunications

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Publication Info

Year
1997
Type
article
Volume
8
Issue
3
Pages
486-504
Citations
402
Access
Closed

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Cite This

Juha Karhunen, Erkki Oja, L. Wang et al. (1997). A class of neural networks for independent component analysis. IEEE Transactions on Neural Networks , 8 (3) , 486-504. https://doi.org/10.1109/72.572090

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DOI
10.1109/72.572090