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Bagged Kernel SOM

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 295)

Abstract

In a number of real-life applications, the user is interested in analyzing non vectorial data, for which kernels are useful tools that embed data into an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). Also, a side effect of the method is that the interpretability of the prototypes is lost. In the present paper, we propose to overcome these two issues by using a bagging approach. The results are illustrated on simulated data sets and compared to alternatives found in the literature.

Keywords

  • Learn Vector Quantization
  • Multiple Kernel
  • Descent Step
  • Topographic Error
  • Dissimilarity Data

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.

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Correspondence to Jérôme Mariette .

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Mariette, J., Olteanu, M., Boelaert, J., Villa-Vialaneix, N. (2014). Bagged Kernel SOM. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-07695-9_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07694-2

  • Online ISBN: 978-3-319-07695-9

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