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An Architecture to Efficiently Learn Co-Similarities from Multi-view Datasets

  • Gilles Bisson
  • Clément Grimal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7663)

Abstract

In this paper, we introduce the MVSim architecture which is able to cluster multi-view datasets (i.e. datasets containing several objects linked together by different relations), by using several instances of a co-similarity algorithm. We show that this framework provides better results than existing approaches, while reducing both time and space complexities thanks to an efficient parallelization of the computations. This approach allows to split large datasets into a set of smaller ones.

Keywords

Multiview Learning Similarity Learning Co-clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gilles Bisson
    • 1
  • Clément Grimal
    • 1
  1. 1.Laboratoire LIG - Batiment CE4Université Joseph Fourier / Grenoble 1 / CNRSGièresFrance

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