A novel single-pass thinning algorithm and an effective set of performance criteria
Pattern Recognition Letters
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Protein function prediction via graph kernels
Bioinformatics
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
A Graph-Theoretic Approach to Enterprise Network Dynamics (Progress in Computer Science and Applied Logic (PCS))
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
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Fast suboptimal algorithms for the computation of graph edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Report on the second symbol recognition contest
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Feature selection for graph-based image classifiers
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A graph matching based approach to fingerprint classification using directional variance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Recursive processing of cyclic graphs
IEEE Transactions on Neural Networks
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An agent-based framework for distributed learning
Engineering Applications of Artificial Intelligence
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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Graphs are a convenient representation formalism for structured objects, but they suffer from the fact that only a few algorithms for graph classification and clustering exist. In this paper a new approach to graph classification by dissimilarity space embedding is proposed. This approach, which is in fact a new graph kernel, allows us to apply advanced classification tools while retaining the high representational power of graphs. The basic idea of the proposed graph kernel is to view the edit distances of a given graph g to a set of training graphs as a vectorial description of g. Once a graph has been transformed into a vector, different dimensionality reduction algorithms are applied such that redundancies are eliminated. To this reduced vectorial data representation any pattern classification algorithms available for feature vectors can be applied. Through various experiments it is shown that the proposed dissimilarity space embedding graph kernel outperforms conventional classification algorithms applied in the original graph domain.