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Bregman Divergences and the Self Organising Map

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Intelligent Data Engineering and Automated Learning – IDEAL 2008 (IDEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5326))

Abstract

We discuss Bregman divergences and the very close relationship between a class of these divergences and the regular family of exponential distributions before applying them to various topology preserving dimension reducing algorithms. We apply these methods to identification of structure in magnetic resonance images of the brain and show that different divergences reveal different structure in these images.

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Author information

Authors and Affiliations

  1. Intelligent Signal Processing Lab, Korea University, Korea

    Eunsong Jang & Hanseok Ko

  2. School of Computing, University of the West of Scotland, UK

    Colin Fyfe

Authors
  1. Eunsong Jang
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  2. Colin Fyfe
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  3. Hanseok Ko
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Editor information

Editors and Affiliations

  1. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

  2. KAIST, Daejeon, Korea

    Dongsup Kim

  3. Brain Science Research Center and Department of Bio & Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, 305-701, Daejeon, Korea

    Soo-Young Lee

  4. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2008 Springer-Verlag Berlin Heidelberg

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Jang, E., Fyfe, C., Ko, H. (2008). Bregman Divergences and the Self Organising Map. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_57

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  • DOI: https://doi.org/10.1007/978-3-540-88906-9_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88905-2

  • Online ISBN: 978-3-540-88906-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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