Geometric models with co-occurrence groups

Joan Bruna, Stéphane Mallat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A geometric model of sparse signal representations is introduced for classes of signals. It is computed by optimizing co-occurrence groups with a maximum likelihood estimate calculated with a Bernoulli mixture model. Applications to face image compression and MNIST digit classification illustrate the applicability of this model.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages259-264
Number of pages6
StatePublished - 2010
Event18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium
Duration: Apr 28 2010Apr 30 2010

Publication series

NameProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

Other

Other18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
CountryBelgium
CityBruges
Period4/28/104/30/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Bruna, J., & Mallat, S. (2010). Geometric models with co-occurrence groups. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 259-264). (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).