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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A New Distance for Data Sets in a Reproducing Kernel Hilbert Space Context
verfasst von : Alberto Muñoz, Gabriel Martos, Javier González
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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In this paper we define distance functions for data sets in a reproduncing kernel Hilbert space (RKHS) context. To this aim we introduce kernels for data sets that provide a metrization of the power set. The proposed distances take into account the underlying generating probability distributions. In particular, we propose kernel distances that rely on the estimation of density level sets of the underlying data distributions, and that can be extended from data sets to probability measures. The performance of the proposed distances is tested on several simulated and real data sets.