TY  - JOUR
AU  - Muhammad Muzzamil Luqman
AU  - Jean-Yves Ramel
AU  - Josep Llados
AU  - Thierry Brouard
PY  - 2013//
TI  - Fuzzy Multilevel Graph Embedding
T2  - PR
JO  - Pattern Recognition
SP  - 551
EP  - 565
VL  - 46
IS  - 2
PB  - Elsevier
KW  - Pattern recognition
KW  - Graphics recognition
KW  - Graph clustering
KW  - Graph classification
KW  - Explicit graph embedding
KW  - Fuzzy logic
N2  - 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.
SN  - 0031-3203
UR  - http://dx.doi.org/10.1016/j.patcog.2012.07.029
N1  - DAG; 600.042; 600.045; 605.203
ID  - Muhammad Muzzamil Luqman2013
ER  -