Similarity and Kernel Matrix Evaluation Based on Spatial Autocorrelation Analysis

  • Vincent Pisetta
  • Djamel A. Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5722)


We extend the framework of spatial autocorrelation analysis on Reproducing Kernel Hilbert Space (RKHS). Our results are based on the fact that some geometrical neighborhood structures vary when samples are mapped into a RKHS, while other neighborhood structures do not. These results allow us to design a new measure for measuring the goodness of a kernel and more generally a similarity matrix. Experiments on UCI datasets show the relevance of our methodology.


Kernel matrix evaluation spatial autocorrelation similarity learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vincent Pisetta
    • 1
  • Djamel A. Zighed
    • 2
  1. 1.RithmeLyonFrance
  2. 2.ERIC LaboratoryBronFrance

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