PerTurbo: A New Classification Algorithm Based on the Spectrum Perturbations of the Laplace-Beltrami Operator

  • Nicolas Courty
  • Thomas Burger
  • Johann Laurent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

PerTurbo, an original, non-parametric and efficient classification method is presented here. In our framework, the manifold of each class is characterized by its Laplace-Beltrami operator, which is evaluated with classical methods involving the graph Laplacian. The classification criterion is established thanks to a measure of the magnitude of the spectrum perturbation of this operator. The first experiments show good performances against classical algorithms of the state-of-the-art. Moreover, from this measure is derived an efficient policy to design sampling queries in a context of active learning. Performances collected over toy examples and real world datasets assess the qualities of this strategy.

Keywords

Support Vector Machine Active Learning Dimensionality Reduction Heat Kernel Gaussian Mixture Model 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Courty
    • 1
  • Thomas Burger
    • 2
  • Johann Laurent
    • 2
  1. 1.Université Européenne de BretagneUniversité de Bretagne SudValoriaFrance
  2. 2.Université Européenne de Bretagne, CNRS, Lab-STICCUniversité de Bretagne SudFrance

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