Graph of words embedding for molecular structure-activity relationship analysis
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
A generic framework for median graph computation based on a recursive embedding approach
Computer Vision and Image Understanding
Improving vector space embedding of graphs through feature selection algorithms
Pattern Recognition
Dimensionality reduction for graph of words embedding
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Speeding up graph edit distance computation through fast bipartite matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Vocabulary selection for graph of words embedding
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
Feature selection on node statistics based embedding of graphs
Pattern Recognition Letters
Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers
ACM Transactions on Information Systems (TOIS)
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
Fuzzy Sets and Systems
Efficient geometric graph matching using vertex embedding
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Note: Distances between sets based on set commonality
Discrete Applied Mathematics
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This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.