Mining Shock Graphs with Kernels

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. Index Terms Kernel methods, object recognition, shock graphs, Support Vector Machines.