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Mining Shock Graphs with Kernels

Frédéric Suard 1, Alain Rakotomamonjy 1, Abdelaziz Bensrhair 1

(22/12/2006)

A common approach for classifying shock graphs is to use a dissimilarity measure on graphs and a distance based classifier. In this paper, we propose the use of kernel functions for data mining problems on shock graphs. The first contribution of the paper is to extend the class of graph kernel by proposing kernels based on bag of paths. Then, we propose a methodology for using these kernels for shock graphs retrieval and other shape mining problems. Our experimental results show that our approach is very competitive compared to graph matching approaches and is rather robust. Others experiments illustrate that with such kernel functions it becomes possible to apply statistical pattern recognition algorithms to shock graphs.

1 :	Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS)
	Institut National des Sciences Appliquées de Rouen – Université du Havre – Université de Rouen : EA4108

Domaine

:

Sciences cognitives/Informatique

Mots Clés : Kernel methods – object recognition – shock graphs – Support Vector Machines

hal-00121988, version 1

http://hal.archives-ouvertes.fr/hal-00121988

oai:hal.archives-ouvertes.fr:hal-00121988

Contributeur : Alain Rakotomamonjy <>

Soumis le : Vendredi 22 Décembre 2006, 23:30:46

Dernière modification le : Vendredi 26 Mars 2010, 10:38:49