A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf
.
A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning
2008
IEEE Transactions on Signal Processing
This paper provides a functional analysis perspective of information-theoretic learning (ITL) by defining bottom-up a reproducing kernel Hilbert space (RKHS) uniquely determined by the symmetric nonnegative definite kernel function known as the cross-information potential (CIP). The CIP as an integral of the product of two probability density functions characterizes similarity between two stochastic functions. We prove the existence of a one-to-one congruence mapping between the ITL RKHS and
doi:10.1109/tsp.2008.2005085
fatcat:tn3bj75wdrdaxkupvbc4ug3nyi