PT Journal
AU Muhammad Muzzamil Luqman
   Jean-Yves Ramel
   Josep Llados
   Thierry Brouard
TI Fuzzy Multilevel Graph Embedding
SO Pattern Recognition
JI PR
PY 2013
BP 551
EP 565
VL 46
IS 2
DI 10.1016/j.patcog.2012.07.029
DE Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic
AB 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.
ER