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Advances in Self-Organizing Maps and Learning Vector Quantization pp 73–82Cite as

Sparse Online Self-Organizing Maps for Large Relational Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

During the last decades , self-organizing maps were proven to be useful tools for exploring data. While the original algorithm was designed for numerical vectors, the data became more and more complex, being frequently too rich to be described by a fixed set of numerical attributes. Several extensions of the original SOM were proposed in the literature for handling kernel or dissimilarity data. Most of them use the entire kernel/dissimilarity matrix, which requires at least quadratic complexity and becomes rapidly unfeasible for 100 000 inputs, for instance. In the present manuscript, we propose a sparse version of the online relational SOM, which sequentially increases the composition of the prototypes.

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References

  1. Kohonen, T.: Self-Organizing Maps, 3rd edn, vol. 30. Springer, Berlin, Heidelberg, New York (2001)

    Google Scholar 

  2. Mac Donald, D., Fyfe, C.: The kernel self organising map. In: Proceedings of 4th International Conference on knowledge-based Intelligence Engineering Systems and Applied Technologies, pp. 317–320 (2000)

    Google Scholar 

  3. Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7–9), 1257–1273 (2008)

    Article  Google Scholar 

  4. Olteanu, M., Villa-Vialaneix, N.: On-line relational and multiple relational SOM. Neurocomputing 147, 15–30 (2015)

    Article  Google Scholar 

  5. Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity data sets. Neural Comput. 22(9), 2229–2284 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  6. Hofmann, D., Schleif, F., Paaßen, B., Hammer, B.: Learning interpretable kernelized prototype-based models. Neurocomputing 141, 84–96 (2014)

    Article  Google Scholar 

  7. Rossi, F., Hasenfuss, A., Hammer, B.: Accelerating relational clustering algorithms with sparse prototype representation. In: Proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 07), Bielefield, Germany, Neuroinformatics Group, Bielefield University (2007)

    Google Scholar 

  8. Gisbrecht, A., Mokbel, B., Hammer, B.: The nystrom approximation for relational generative topographic mappings. NIPS Workshop on Challenges of Data Visualization (2010)

    Google Scholar 

  9. Mariette, J., Olteanu, M., Boelaert, J., Villa-Vialaneix, N.: Bagged kernel SOM. In: Villmann, T., Schleif, F., Kaden, M., Lange, M. (eds.) Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014). Volume 295 of Advances in Intelligent Systems and Computing, Mittweida, Germany. Springer, Berlin, Heidelberg, pp. 45–54 (2014)

    Google Scholar 

  10. Goldfarb, L.: A unified approach to pattern recognition. Pattern Recogn. 17(5), 575–582 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  11. Rossi, F.: How many dissimilarity/kernel self organizing map variants do we need? In: Villmann, T., Schleif, F., Kaden, M., Lange, M. (eds.) Advances in Self-Organizing Maps and Learning Vector Quantization (Proceedings of WSOM 2014). Volume 295 of Advances in Intelligent Systems and Computing, Mittweida, Germany. Springer, Berlin, Heidelberg, pp. 3–23 (2014)

    Google Scholar 

  12. Aronszajn, N.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337–404 (1950)

    Article  MATH  MathSciNet  Google Scholar 

  13. Hebert, P.D.N., Penton, E.H., Burns, J.M., Janzen, D.H., Hallwachs, W.: Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly astraptes fulgerator. Genetic Analysis (2004)

    Google Scholar 

  14. Kimura, M.: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16(2), 111–120 (1980)

    Article  Google Scholar 

  15. Rousset, P., Giret, J.F.: Classifying qualitative time series with SOM: the typology of career paths in France. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds.) Computational and Ambient Intelligence. Lecture Notes in Computer Science, vol. 4507, pp. 757–764. Springer, Berlin, Heidelberg (2007)

    Google Scholar 

  16. Abbott, A., Forest, J.: Optimal matching methods for historical sequences. J. Interdisc. Hist. 16(3), 471–494 (1986)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. SAMM - Université Paris 1 Panthéon-Sorbonne, 90, Rue de Tolbiac, 75013, Paris, France

    Madalina Olteanu

  2. INRA, UR 0875 MIAT, BP 52627, 31326, Castanet Tolosan, France

    Nathalie Villa-Vialaneix

Authors
  1. Madalina Olteanu
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  2. Nathalie Villa-Vialaneix
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Corresponding authors

Correspondence to Madalina Olteanu or Nathalie Villa-Vialaneix .

Editor information

Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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© 2016 Springer International Publishing Switzerland

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Olteanu, M., Villa-Vialaneix, N. (2016). Sparse Online Self-Organizing Maps for Large Relational Data. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

  • eBook Packages: EngineeringEngineering (R0)

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