Discriminative prototype selection methods for graph embedding

  • Authors:

  • Ehsan Zare Borzeshi;Massimo Piccardi;Kaspar Riesen;Horst Bunke

  • Affiliations:

  • School of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Ultimo, 2007 NSW, Australia;School of Computing and Communications, Faculty of Engineering and Information Technology, University of Technology, Sydney (UTS), Ultimo, 2007 NSW, Australia;Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600 Olten , Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of ''prototype'' graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches.