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Characteristic Kernels on Groups and Semigroups (2009)

Abstract
Embeddings of random variables in reproducing kernel Hilbert spaces (RKHSs) may be used to conduct statistical inference based on higher order moments. For sufficiently rich (characteristic) RKHSs, each probability distribution has a unique embedding, allowing all statistical properties of the distribution to be taken into consideration. Necessary and sufficient conditions for an RKHS to be characteristic exist for Rn. In the present work, conditions are established for an RKHS to be characteristic on groups and semigroups. Illustrative examples are provided, including characteristic kernels on periodic domains, rotation matrices, and Rn+.

Details der Publikation
Download http://eprints.pascal-network.org/archive/00004347/
Archiv PASCAL EPrints (United Kingdom)
Keywords Computational, Information-Theoretic Learning with Statistics, Learning/Statistics & Optimisation, Brain Computer Interfaces, Theory & Algorithms
Typ Conference or Workshop Item, PeerReviewed
Verknüpfungen http://eprints.pascal-network.org/archive/00004347/01/NIPS2008-Fukumizu_5466[0].pdf