PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition
Somayeh Danafar, Arthur Gretton and Juergen Schmidhuber
In: Machine Learning and Knowledge Discovery in Databases(2010).

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 statis- tical 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 ker- nels outperform previous methods on robotics problems and also on a widely used benchmark for recognition of human actions in videos.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7954
Deposited By:Arthur Gretton
Deposited On:17 March 2011