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A nonlinear extension of the Generalized Hebbian learning

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Abstract

In this letter, we introduce a nonlinear hierarchic PCA type neural network with a simple architecture. The learning algorithm is a kind of nonlinear extension of the well-known Sanger's Generalized Hebbian Algorithm (GHA). It is derived from a nonlinear optimization criterion. Experiments with sinusoidal data show that the neurons become sensitive to different sinusoids. Standard linear PCA algorithms don't have such a separation property.

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References

  1. A. Cichocki, R. Unbehauen.Neural networks for optimization and signal processing, Wiley, New York, 1993.

    Google Scholar 

  2. S. Haykin.Neural networks: a comprehensive foundation, IEEE Press and Macmillan, New York, 1994.

    Google Scholar 

  3. T.D. Sanger. Optimal unsupervised learning in a single-layer linear feedforward neural network,Neural Networks vol. 2, pp. 459–473, 1989.

    Article  Google Scholar 

  4. J. Karhunen, J. Joutsensalo. Nonlinear generalizations of principal component learning algorithms,Proc. 1993 Int. Joint Conf. on Neural Networks (Nagoya, Japan) vol. 3, pp. 2599–2602, 1993.

    Google Scholar 

  5. J. Karhunen. Optimization criteria and nonlinear PCA neural networks,Proc. 1994 IEEE Int. Conf. on Neural Networks (Orlando) vol. II, pp. 1242–1246, 1994.

    Google Scholar 

  6. E. Oja, H. Ogawa, J. Wangviwattana. Learning in nonlinear constrained Hebbian networks, in T. Kohonen et al. eds.,Artificial Neural Networks, North-Holland, Amsterdam, pp. 385–390, 1991.

    Google Scholar 

  7. J. Karhunen, J. Joutsensalo. Representation and separation of signals using nonlinear PCA type learning,Neural Networks vol. 7, pp. 113–127, 1994.

    Article  Google Scholar 

  8. F. Palmieri. Hebbian learning and self-association in nonlinear neural networks,Proc. 1994 IEEE Int. Conf. on Neural Networks (Orlando) vol. II, pp. 1258–1263, 1994.

    Google Scholar 

  9. C.W. Therrien.Discrete random signals and statistical signal processing, Prentice-Hall, Englewood Cliffs, NJ, 1992.

    Google Scholar 

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Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinky University of Technology, Rakentajanaukio 2 C, FIN-02150, Espoo, Finland

    Jyrki Joutsensalo & Juha Karhunen

Authors
  1. Jyrki Joutsensalo
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  2. Juha Karhunen
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Joutsensalo, J., Karhunen, J. A nonlinear extension of the Generalized Hebbian learning. Neural Process Lett 2, 5–8 (1995). https://doi.org/10.1007/BF02312375

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  • DOI: https://doi.org/10.1007/BF02312375

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