Kernel Spectral Clustering for Dynamic Data

  • Diego Hernán Peluffo-Ordóñez
  • Sergio García-Vega
  • Andrés Marino Álvarez-Meza
  • César Germán Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

This paper introduces a novel spectral clustering approach based on kernels to analyze time-varying data. Our approach is developed within a multiple kernel learning framework, which, in this case is assumed as a linear combination model. To perform such linear combination, weighting factors are estimated by a ranking procedure yielding a vector calculated from the eigenvectors-derived-clustering-method. Particularly, the method named kernel spectral clustering is considered. Proposed method is compared to some conventional spectral clustering techniques, namely, kernel k-means and min-cuts. Standard k-means as well. The clustering performance is quantified by the normalized mutual information and Adjusted Rand Index measures. Experimental results prove that proposed approach is an useful tool for both tracking and clustering dynamic data, being able to manage applications for human motion analysis.

Keywords

Dynamic data kernels support vector machines spectral clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Diego Hernán Peluffo-Ordóñez
    • 1
  • Sergio García-Vega
    • 1
  • Andrés Marino Álvarez-Meza
    • 1
  • César Germán Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de Colombia Sede ManizalesColombia

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