Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition

  • Somayeh Danafar
  • Arthur Gretton
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6321)


Embedding probability distributions into a sufficiently rich (characteristic) reproducing kernel Hilbert space enables us to take higher order statistics into account. Characterization also retains effective statistical relation between inputs and outputs in regression and classification. Recent works established conditions for characteristic kernels on groups and semigroups. Here we study characteristic kernels on periodic domains, rotation matrices, and histograms. Such structured domains are relevant for homogeneity testing, forward kinematics, forward dynamics, inverse dynamics, etc. Our kernel-based methods with tailored characteristic kernels outperform previous methods on robotics problems and also on a widely used benchmark for recognition of human actions in videos.


Characteristic kernels Locally compact Abelian groups Rotation matrices Semigroups Recognition of human actions in videos 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Somayeh Danafar
    • 1
  • Arthur Gretton
    • 2
    • 3
  • Jürgen Schmidhuber
    • 1
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)Manno-LuganoSwitzerland
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.MPI for Biological CyberneticsTübingenGermany

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