Neural networks for pattern recognition
Neural networks for pattern recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Mining Graph Data
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein function prediction via graph kernels
Bioinformatics
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A Riemannian approach to graph embedding
Pattern Recognition
Prototype selection for dissimilarity-based classifiers
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
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
Recursive processing of cyclic graphs
IEEE Transactions on Neural Networks
On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
On improving dissimilarity-based classifications using a statistical similarity measure
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
An empirical evaluation on dimensionality reduction schemes for dissimilarity-based classifications
Pattern Recognition Letters
Improving vector space embedding of graphs through feature selection algorithms
Pattern Recognition
Note: Distances between sets based on set commonality
Discrete Applied Mathematics
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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 we propose a new approach to graph classification by embedding graphs in real vector spaces. This approach allows us to apply advanced classification tools while retaining the high representational power of graphs. The basic idea of our approach is to regard the edit distances of a given graph gto 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, pattern classification algorithms can be applied. Through various experimental results we show that the proposed vector space embedding and subsequent classification with the reduced vectors outperform the classification algorithms in the original graph domain.