Abstract
Blind source separation has been a topic of great interest for researchers in the last few years, and applications are starting to appear. Until now, most of the research and applications have focused on the separation of linear mixtures. In this paper we briefly discuss the problem of separation of nonlinear mixtures, and present a method for performing this kind of separation. We also present some experimental results.
This work was partially supported by PRAXIS project TIT/1585/95.
Also with ISEL.
Also with IST.
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Almeida, L. B.: Multilayer perceptrons. Handbook of Neural Computation. Fiesler, E. and Beale, R., eds. Institute of Physics and Oxford University Press (1997) available at http://www.oup-usa.org/acadref/ncc1_2.pdf.
Bell, A. and Sejnowski, T.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7 (1995) 1129–1159. Separation. Aussois, France. Cardoso, J. F., Jutten, C., and Loubaton, P., eds. (1999) 277–282.
Comon, P.: Independent component analysis—A new concept?. Signal Processing 36 (1994) 287–314.
Deco, G. and Brauer, W.: Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures. Neural Networks 8 (1995) 525–535.
Hochreiter, S. and Schmidhuber, J.: LOCOCODE performs nonlinear ICA without knowing the number of sources. Proc. First Int. Worksh. Independent Component Analysis and Signal Separation. Aussois, France. Cardoso, J. F., Jutten, C., and Loubaton, P., eds. (1999) 277–282.
Lee, T.-W., Girolami, M., Bell, A., and Sejnowski, T.: An unifying information-theoretic framework for independent component analysis. International Journal on Mathematical and Computer Modeling (1998).
Marques, G. C. and Almeida, L. B.: An objective function for independence. Proc. International Conference on Neural Networks. Washington DC (1996) 453–457.
Marques, G. C. and Almeida, L. B.: Separation of nonlinear mixtures using pattern repulsion. Proc. First Int. Worksh. Independent Component Analysis and Signal
Pajunen, P.: Nonlinear independent component analysis by self-organizing maps. Proc. Int. Conf. on Artificial Neural Networks. Bochum, Germany (1996) 815–819.
Palmieri, F., Mattera, D., and Budillon, A.: Multi-layer independent component analysis (MLICA). Proc. First Int. Worksh. Independent Component Analysis and Signal Separation. Aussois, France. Cardoso, J. F., Jutten, C., and Loubaton, P., eds. (1999) 93–97.
Xu, D., Principe, J., Fisher, J., and Wu H.-C.: A novel measure for independent component analysis. Proc. IEEE Int. Conf. Acoust., Speech and Sig. Processing. Seattle WA 2 (1998) 1161–1164.
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Almeida, L.B., Marques, G.C. (1999). Nonlinear blind source separation by pattern repulsion. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100535
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DOI: https://doi.org/10.1007/BFb0100535
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