Improved SOM Learning Using Simulated Annealing (original) (raw)

Abstract

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Rizzo, R., Chella, A.: A Comparison between Habituation and Conscience Mechanism in Self-Organizing Maps. IEEE Transactions on neural networks 17(3), 807–810 (2006)
    Article Google Scholar
  2. DeSieno, D.: Adding a conscience to copetitive learning. In: Proc. ICNN’88, International conference on Neural Networks, pp. 117–124. IEEE Computer Society Press, Piscataway, NJ (1988)
    Chapter Google Scholar
  3. Berglund, E., Sitte, J.: The parameterless self-organizing map algorithm. IEEE Transactions on neural networks 17(2), 305–316 (2006)
    Article Google Scholar
  4. Haese, K.: Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps. Neural Comput. 13(3), 595–619 (2001)
    Article MATH Google Scholar
  5. Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)
    Article Google Scholar
  6. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
    Article Google Scholar
  7. Graepel, T., Burger, M., Obermayer, K.: Self-organizing maps: generalizations and new optimization techniques. Neurocomputing 21, 173–190 (1998)
    Article MATH Google Scholar
  8. Douzono, H., Hara, S., Noguchi, Y.: A Clustering Method of Chromosome Fluorescence Profiles Using Modified Self Organizing Map Controlled by Simulated Annealing. In: IJCNN’00. IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 4 (2000)
    Google Scholar
  9. Haese, K.: Kalman filter implementation of self-organizing feature maps. Neural Comput. 11(5), 1211–1233 (1999)
    Article Google Scholar
  10. Haese, K.: Self-organizing feature map with self-adjusting learning parameters. IEEE Transactions on Neural Network 9(6), 1270–1278 (1998)
    Article Google Scholar
  11. Berglund, E., Sitte, J.: The parameter-less SOM algorithm. In: Proc. ANZIIS, pp. 159–164 (2003)
    Google Scholar
  12. Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
    Google Scholar
  13. Van Hulle, M. (ed.): Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization. John Wiley, New York (2000)
    Google Scholar
  14. Ingber, L.: Very fast simulated re-annealing. Journal of Mathl. Comput. Modelling 12(8), 967–973 (1989)
    Article MATH Google Scholar
  15. Ingber, L.: Adaptive simulated annealing (ASA): lessons learned. J. Control and Cybernetics 25(1), 33–54 (1996)
    MATH Google Scholar
  16. Goppert, J., Rosenstiel, W.: Regularized SOM-Training: A Solution to the Topology-Approximation dilemma, University of Tbingen (1996)
    Google Scholar
  17. National Cancer Institute, DTP AIDS antiviral screen dataset [online], http://dtp.nci.nih.gov/docs/aids/aids/data.html
  18. Di Fatta, G., Fiannaca, A., Rizzo, R., Urso, A., Berthold, M.R., Gaglio, S.: Context-Aware Visual Exploration of Molecular Databases. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 136–141. Springer, Heidelberg (2006)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. ICAR-CNR, Consiglio Nazionale delle Ricerche, Palermo, Italy
    Antonino Fiannaca, Salvatore Gaglio, Riccardo Rizzo & Alfonso M. Urso
  2. School of Systems Engineering, Univeristy of Reading, UK
    Giuseppe Di Fatta
  3. Dipartimento di Ingegneria Informatica, Universitá di Palermo, Italy
    Salvatore Gaglio

Authors

  1. Antonino Fiannaca
  2. Giuseppe Di Fatta
  3. Salvatore Gaglio
  4. Riccardo Rizzo
  5. Alfonso M. Urso

Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

Rights and permissions

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fiannaca, A., Di Fatta, G., Gaglio, S., Rizzo, R., Urso, A.M. (2007). Improved SOM Learning Using Simulated Annealing. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4\_29

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us