Characteristic kernels on structured domains excel in robotics and human action recognition

Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


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. © 2010 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages16
StatePublished - Nov 5 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Characteristic kernels on structured domains excel in robotics and human action recognition'. Together they form a unique fingerprint.

Cite this