Abstract
This contribution presents a new approach to recover original signals (“sources”) from their linear mixtures, observed by the same number of sensors. The algorithm proposed assume that the input distributions are bounded and the sources generate certain combinations of boundary values. The method is simpler than other proposals and is based on geometric algebra properties. We present a neural network approach to show that with two networks, one for the separation of sources and one for weight learning, running in parallel, it is possible to efficiently recover the original signals. The learning rule is unsupervised and each computational element uses only local information.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
S. Amari, A. Cichocki, H.H. Yang, “A new learning algorithm for blind signal separation”. Advances in Neural Information Processing Systems (1995), Vol.8. MIT Press: Cambridge, MA, pp. 757–763, 1996.
A. J. Bell, T. J. Sejnowski, “An information-maximisation approach to blind separation and blind deconvolution”. Neural Computation 7, pp. 1129–1159. 1995.
C. Jutten, J. Hérault, P. Comon and E. Sorouchiary, “Blind separation of sources”, Parts I, II and III. Signal Processing, vol. 24, no. 1, pp.1–29, July 1991.
K. Matsuoka, M. Ohya, M. Kawamoto, “A neural net for blind separation of nonstationary signals”. Neural Networks, vol. 8, no. 3, pp. 411–419, 1995.
A.M. Peinado, J.M. Lopez, V.E. Sánchez, J.C. Segura and A.J. Rubio, “Improvements in HMM-based isolated word recognition systems”, IEE Proceedings-I, Vol. 138, No.3, pp.201–206, June, 1991.
C. G. Puntonet, A. Prieto, C. Jutten, M. Rodríguez-Alvarez, J. Ortega, “Separation of sources: a geometry-based procedure for reconstruction of n-valued signals”. Signal Processing, vol. 46, no. 3, pp. 267–284. 1995.
Puntonet,C.G.; Prieto,A.: “An adaptive geometrical procedure for blind separation of sources”, Neural Processing Letters, Vol.2, No.5, pp. 23–27, Sept. 1995.
C.G.Puntonet, A.Prieto, J. Ortega. “New geometrical approach for blind separation of sources mapped to a neural network”. Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP-96), Venecia, Italy, IEEE Computer Soc. Press, pp. 174–182, 21–23 August, 1996
C.G. Puntonet, A. Mansour and C. Jutten, “Un algorithme géométrique pour la séparation de sources. 15th GRETSI. Juan-Les-pins (France), Sept., 18–21, 1995.
Rumelhart,D.E.; Zipser,D., Feature discovery by Competitive learning. In: Parallel Distributed processing, Rumelhart,D.E. & McClelland, J.L., MIT Press;, Vol. I, 151–193, 1986.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prieto, A., Puntonet, C.G., Prieto, B., Rodríguez-Alvarez, M. (1997). A competitive neural network for blind separation of sources based on geometric properties. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032569
Download citation
DOI: https://doi.org/10.1007/BFb0032569
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63047-0
Online ISBN: 978-3-540-69074-0
eBook Packages: Springer Book Archive