%0 Journal Article
%T Fuzzy Multilevel Graph Embedding
%A Muhammad Muzzamil Luqman
%A Jean-Yves Ramel
%A Josep Llados
%A Thierry Brouard
%J Pattern Recognition
%D 2013
%V 46
%N 2
%I Elsevier
%@ 0031-3203
%F Muhammad Muzzamil Luqman2013
%O DAG; 600.042; 600.045; 605.203
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2270), last updated on Thu, 14 Apr 2016 17:15:49 +0200
%X Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs.
%K Pattern recognition
%K Graphics recognition
%K Graph clustering
%K Graph classification
%K Explicit graph embedding
%K Fuzzy logic
%U http://dx.doi.org/10.1016/j.patcog.2012.07.029
%P 551-565