Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
A Shape Analysis Model with Applications to a Character Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining Graph Data
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
A Graph-Theoretic Approach to Enterprise Network Dynamics (Progress in Computer Science and Applied Logic (PCS))
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Reducing the Dimensionality of Vector Space Embeddings of Graphs
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Feature Ranking Algorithms for Improving Classification of Vector Space Embedded Graphs
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
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
Graph Classification and Clustering Based on Vector Space Embedding
Graph Classification and Clustering Based on Vector Space Embedding
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
Reactive tabu search for measuring graph similarity
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Maximum likelihood method for parameter estimation of bell-shaped functions on graphs
Pattern Recognition Letters
Graph complexity from the jensen-shannon divergence
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Improving fuzzy multilevel graph embedding through feature selection technique
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Advanced Engineering Informatics
Depth-based complexity traces of graphs
Pattern Recognition
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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Graph based pattern representation offers a versatile alternative to vectorial data structures. Therefore, a growing interest in graphs can be observed in various fields. However, a serious limitation in the use of graphs is the lack of elementary mathematical operations in the graph domain, actually required in many pattern recognition algorithms. In order to overcome this limitation, the present paper proposes an embedding of a given graph population in a vector space R^n. The key idea of this embedding approach is to interpret the distances of a graph g to a number of prototype graphs as numerical features of g. In previous works, the prototypes were selected beforehand with heuristic selection algorithms. In the present paper we take a more fundamental approach and regard the problem of prototype selection as a feature selection or dimensionality reduction problem, for which many methods are available. With several experiments we show the feasibility of graph embedding based on prototypes obtained from such feature selection algorithms and demonstrate their potential to outperform previous approaches.