Space-time local embeddings

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

Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on nonmetric datasets show that more information can be preserved in space-time.


Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG - Genève
Institut:
CRAG - Centre de Recherche Appliquée en Gestion
Classification:
Informatique
Adresse bibliogr.:
Montréal, Canada, 11th December 2015
Date:
Montréal, Canada
11th December 2015
2015
Pagination:
9 p.
Publié dans
Advances in Neural Information Processing Systems 28 : Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada
Ressource(s) externe(s):
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 Notice créée le 2016-08-22, modifiée le 2018-12-07

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