Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition
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.
Cite
Text
Danafar et al. "Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15880-3_23Markdown
[Danafar et al. "Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/danafar2010ecmlpkdd-characteristic/) doi:10.1007/978-3-642-15880-3_23BibTeX
@inproceedings{danafar2010ecmlpkdd-characteristic,
title = {{Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition}},
author = {Danafar, Somayeh and Gretton, Arthur and Schmidhuber, Jürgen},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {264-279},
doi = {10.1007/978-3-642-15880-3_23},
url = {https://mlanthology.org/ecmlpkdd/2010/danafar2010ecmlpkdd-characteristic/}
}