ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Structure preserving embedding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Graph Classification and Clustering Based on Vector Space Embedding
Graph Classification and Clustering Based on Vector Space Embedding
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Graph embedding for pattern recognition
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
A fuzzy-interval based approach for explicit graph embedding
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
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
Multiple classifiers for graph of words embedding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Graph matching – challenges and potential solutions
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Feature selection on node statistics based embedding of graphs
Pattern Recognition Letters
Fuzzy multilevel graph embedding
Pattern Recognition
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Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named "fuzzy multilevel graph embedding - FMGE", through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.