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

Danafar, S; Gretton, A; Schmidhuber, J

doi:10.1007/978-3-642-15880-3_23

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

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Gretton, A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Danafar, S., Gretton, A., & Schmidhuber, J. (2010). Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. In J. Balcázar, F. Bonchi, A. Gionis, & M. Sebag (Eds.), ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010 (pp. 264-279). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/21.11116/0000-0007-8BD7-A

Abstract

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.