Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isotree: Tree clustering via metric embedding
Neurocomputing
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bipartite graph matching for computing the edit distance of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Kernels For Structured Data
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Isomap emerged as a powerful tool for analyzing input patterns on manifolds of the underlying space. It builds a neighborhood graph derived from the observable distance information and recomputes pairwise distances as the shortest path on the neighborhood graph. In the present paper, Isomap is applied to graph based pattern representations. For measuring pairwise graph dissimilarities, graph edit distance is used. The present paper focuses on classification and employs a support vector machine in conjunction with kernel values derived from original and Isomap graph edit distances. In an experimental evaluation on five different data sets from the IAM graph database repository, we show that in four out of five cases the graph kernel based on Isomap edit distance performs superior compared to the kernel relying on the original graph edit distances.