@Article{MuhammadMuzzamilLuqman2013,
author="Muhammad Muzzamil Luqman
and Jean-Yves Ramel
and Josep Llados
and Thierry Brouard",
title="Fuzzy Multilevel Graph Embedding",
journal="Pattern Recognition",
year="2013",
publisher="Elsevier",
volume="46",
number="2",
pages="551--565",
optkeywords="Pattern recognition",
optkeywords="Graphics recognition",
optkeywords="Graph clustering",
optkeywords="Graph classification",
optkeywords="Explicit graph embedding",
optkeywords="Fuzzy logic",
abstract="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.",
optnote="DAG; 600.042; 600.045; 605.203",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2270), last updated on Thu, 14 Apr 2016 17:15:49 +0200",
issn="0031-3203",
doi="10.1016/j.patcog.2012.07.029",
opturl="http://dx.doi.org/10.1016/j.patcog.2012.07.029"
}