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Large Margin Local Metric Learning

  • Julien Bohné
  • Yiming Ying
  • Stéphane Gentric
  • Massimiliano Pontil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

Linear metric learning is a widely used methodology to learn a dissimilarity function from a set of similar/dissimilar example pairs. Using a single metric may be a too restrictive assumption when handling heterogeneous datasets. Recently, local metric learning methods have been introduced to overcome this limitation. However, they are subjects to constraints preventing their usage in many applications. For example, they require knowledge of the class label of the training points. In this paper, we present a novel local metric learning method, which overcomes some limitations of previous approaches. The method first computes a Gaussian Mixture Model from a low dimensional embedding of training data. Then it estimates a set of local metrics by solving a convex optimization problem; finally, a dissimilarity function is obtained by aggregating the local metrics. Our experiments show that the proposed method achieves state-of-the-art results on four datasets.

Keywords

Dissimilarity learning local metric learning face recognition nearest neighbor classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Julien Bohné
    • 1
    • 2
  • Yiming Ying
    • 3
  • Stéphane Gentric
    • 2
  • Massimiliano Pontil
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Safran MorphoIssy-les-MoulineauxFrance
  3. 3.Department of Computer ScienceUniversity of ExceterExceterUK

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