Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Protein function prediction via graph kernels
Bioinformatics
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Feature selection for graph-based image classifiers
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Graph matching using the interference of discrete-time quantum walks
Image and Vision Computing
Image classification using marginalized kernels for graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding using quantum commute times
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Kernel fusion for image classification using fuzzy structural information
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Network ensemble clustering using latent roles
Advances in Data Analysis and Classification
Graph embedding using a quasi-quantum analogue of the hitting times of continuous time quantum walks
Quantum Information & Computation
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
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Random walk kernels in conjunction with Support Vector Machines are powerful methods for error-tolerant graph matching. Because of their local definition, however, the applicability of random walk kernels strongly depends on the characteristics of the underlying graph representation. In this paper, we describe a simple extension to the standard random walk kernel based on graph edit distance. The idea is to include global matching information in the local similarity evaluation of random walks in graphs. The proposed extension allows us to improve the performance of the random walk kernel significantly. We present an experimental evaluation of our method on three difficult graph datasets.