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Improved PLSOM algorithm

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Abstract

The original Parameter-Less Self-Organising Map (PLSOM) algorithm was introduced as a solution to the problems the Self-Organising Map (SOM) encounters when dealing with certain types of mapping tasks. Unfortunately the PLSOM suffers from over-sensitivity to outliers and over-reliance on the initial weight distribution. The PLSOM2 algorithm is introduced to address these problems with the PLSOM. PLSOM2 is able to cope well with outliers without exhibiting the problems associated with the standard PLSOM algorithm. The PLSOM2 requires very little computational overhead compared to the standard PLSOM, thanks to an efficient method of approximating the diameter of the inputs, and does not rely on a priori knowledge of the training input space. This paper provides a discussion of the reasoning behind the PLSOM2 and experimental results showing its effectiveness for mapping tasks.

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References

  1. Wilson DR, Martinez TR (2001) The need for small learning rates on large problems. In: Proc int joint conf on neural networks, vol 1, 2001, pp 115–119

  2. Berglund E, Sitte J (2006) The parameter-less self-organizing map algorithm. IEEE Trans Neural Netw 17(2):305–316

    Article  Google Scholar 

  3. Kohonen T (1984) Self-organization and associative memory, vol 8. Springer, Berlin

    MATH  Google Scholar 

  4. Oja M, Kaski S, Kangas J, Kohonen T (2007) Bibliography of SOM papers. http://www.cis.hut.fi/research/som-bibl/. 2007

  5. Göppert J, Rosenstiel W (1996) Varying cooperation in SOM for improved function approximation. In: IEEE int conf on neural networks, vol 1, 1996, pp 1–6

  6. Lang R, Warwick K (2002) The plastic self organising map. In: Int joint conf on neural networks, vol 1, 2002, pp 727–732

  7. Su M-C, Chang H-T (2000) Fast self-organizing feature map algorithm. IEEE Trans Neural Netw 11(3):721–733

    Article  Google Scholar 

  8. Fritzke B (1994) A growing neural gas network learns topologies. In: Advances in neural information processing systems 7, 1994, pp 625–632

  9. Fritzke B (1994) Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Netw 7(9):1441–1460

    Article  Google Scholar 

  10. Iglesias R, Barro S (1999) SOAN: self organizing with adaptive neighborhood neural network. In: Int work-conf on artificial and natural neural networks, vol 1, 1999, pp 591–600

  11. Haese K (1999) Kalman filter implementation of self-organizing feature maps. Neural Comput 11(5):1211–1233

    Article  Google Scholar 

  12. Haese K, Goodhill GJ (2001) Auto-SOM: recursive parameter estimation for guidance of self-organizing feature maps. Neural Comput 13(3):595–619

    Article  MATH  Google Scholar 

  13. Starzyk JA, Zhu Z, Liu T-H (2005) Self-organizing learning array. IEEE Trans Neural Netw 16(2):355–363

    Article  Google Scholar 

  14. Kwok T, Smith KA (2004) A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization. IEEE Trans Neural Netw 15(1):84–98

    Article  Google Scholar 

  15. Shah-Hosseini H, Safabakhsh R (2000) TASOM: the time adaptive self-organizing map. In: Int conf on information technology: coding and computing, 2000, pp 422–427

  16. Shah-Hosseini H, Safabakhsh R (2003) TASOM: a new time adaptive self-organizing map. IEEE Trans Syst Man, Cybern Part B 33(2):271–282

    Article  Google Scholar 

  17. Venkatesh YV, Raja SK, Ramya N (2006) Multiple contour extraction from graylevel images using an artificial neural network. IEEE Trans Image Process 15(4):892–899

    Article  Google Scholar 

  18. Berglund E, Sitte J (2005) Sound source localisation through active audition. In: Int conf on intelligent robots and systems, 2005, pp 653–658

  19. Berglund E, Sitte J, Wyeth G (2007) Active audition using the parameter-less self-organising map. Auton Robots 24(4):401–417

    Article  Google Scholar 

  20. DeSieno D (1988) Adding a conscience to competitive learning. In: IEEE international conference on neural networks, vol 1, 1988, pp 117–124

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Correspondence to Erik Berglund.

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Berglund, E. Improved PLSOM algorithm. Appl Intell 32, 122–130 (2010). https://doi.org/10.1007/s10489-008-0138-7

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  • DOI: https://doi.org/10.1007/s10489-008-0138-7

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