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Hellinger divergence in information theoretic novelty detection

  • In this work a novelty detection framework provided by M. Filippone and G. Sanguinetti is considered, which is useful especially when only few training samples are available. It is restricted to Gaussian mixture models and makes use of information theory, applying the Kullback-Leibler divergence. In this work two variations of the framework are presented, applying the symmetric Hellinger divergence and a statistical likelihood approach.

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Metadaten
Author:Paul Stürmer
URN:urn:nbn:de:bsz:mit1-opus-46323
Document Type:Master's Thesis
Language:English
Date of Publication (online):2014/11/24
Publishing Institution:Hochschule Mittweida
Release Date:2014/11/24
GND Keyword:Wahrscheinlichkeitsverteilung
Institutes:03 Mathematik / Naturwissenschaften / Informatik
Dewey Decimal Classification:510 Mathematik
Access Rights:Innerhalb der Hochschule
Licence (German):License LogoEs gilt das UrhG