Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique

  • Muhammad Muzzamil Luqman
  • Jean Yves Ramel
  • Josep Lladós
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


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.


graphics recognition graph classification explicit graph embedding feature selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Muhammad Muzzamil Luqman
    • 1
    • 2
  • Jean Yves Ramel
    • 1
  • Josep Lladós
    • 2
  1. 1.Laboratoire d’InformatiqueUniversité François Rabelais de ToursFrance
  2. 2.Computer Vision CenterUniversitat Autònòma de BarcelonaSpain

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