Information geometry and minimum description length networks

Sun, Ke ( Computer Vision and Multimedia Laboratory, University of Geneva, Switzerland) ; Wang, Jun ( Expedia, Switzerland) ; Kalousis, Alexandros ( Haute école de gestion de Genève, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Marchand-Maillet, Stéphane ( Computer Vision and Multimedia Laboratory, University of Geneva, Switzerland )

We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations.


Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG GE Haute école de gestion de Genève
Institut:
CRAG - Centre de Recherche Appliquée en Gestion
Classification:
Informatique
Adresse bibliogr.:
[S.l.] , Journal of machine learning research
Date:
[S.l.]
Journal of machine learning research
2015
Pagination:
10 p.
Titre du document hôte:
Journal of machine learning research : proceedings of the 32nd International Conference on Machine Learning, pp. 49–58, 2015
Numérotation (vol. no.):
2015, vol. 37, pp. 49–58
ISSN:
1938-7228
Ressource(s) externe(s):
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 Notice créée le 2015-11-12, modifiée le 2018-04-09

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