Dissimilarity-Based Multiple Instance Learning

  • Lauge Sørensen
  • Marco Loog
  • David M. J. Tax
  • Wan-Jui Lee
  • Marleen de Bruijne
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernel-based approaches and therefore allows for the usage of a wider range of proximity measures. Two conceptually different types of dissimilarity measures are considered: one based on point set distance measures and one based on the earth movers distance between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results.

Keywords

dissimilarity representation multiple instance learning bag dissimilarity measure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lauge Sørensen
    • 1
  • Marco Loog
    • 1
    • 2
  • David M. J. Tax
    • 2
  • Wan-Jui Lee
    • 2
  • Marleen de Bruijne
    • 1
    • 3
  • Robert P. W. Duin
    • 2
  1. 1.The Image Group, Department of Computer ScienceUniversity of CopenhagenDenmark
  2. 2.Pattern Recognition LaboratoryDelft University of TechnologyThe Netherlands
  3. 3.Departments of Radiology & Medical Informatics, Erasmus MCBiomedical Imaging Group RotterdamRotterdamThe Netherlands

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