Dissimilarity-based multiple instance learning

Authors:
Lauge Sørensen;Marco Loog;David M. J. Tax;Wan-Jui Lee;Marleen de Bruijne;Robert P. W. Duin
Affiliations:
Department of Computer Science, University of Copenhagen, Denmark;Department of Computer Science, University of Copenhagen, Denmark and Pattern Recognition Laboratory, Delft University of Technology, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, The Netherlands;Department of Computer Science, University of Copenhagen, Denmark and Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, The Netherlands
Venue:
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Year:
2010

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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 kernelbased 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 withinand 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.