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Abstract
We introduce a general criterion for blindly extracting a subset of sources in instantaneous mixtures. We derive the corresponding estimation equations and generalize them based on arbitrary nonlinear separating functions. A quasi-Newton algorithm for minimizing the criterion is presented, which reduces to the FastICA algorithm in the case when only one source is extracted. The asymptotic distribution of the estimator is obtained and a simulation example is provided.
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References
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Authors and Affiliations
Laboratoire de Modélisation et Calcul, BP 53, 38041 Cedex, Grenoble, France
Dinh-Tuan Antoine Pham
- Dinh-Tuan Antoine Pham
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Editors and Affiliations
Siemens Corporate Research, 755 College Road East, 08540, Princeton, NJ, USA
Justinian Rosca
Department of CSEE, Oregon Health and Science University, Portland, Oregon, USA
Deniz Erdogmus
Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
José C. Príncipe
McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada
Simon Haykin
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© 2006 Springer-Verlag Berlin Heidelberg
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Pham, DT.A. (2006). Blind Partial Separation of Instantaneous Mixtures of Sources. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_108
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