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Fuzzy multilevel graph embedding
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DOI：10.1016/j.patcog.2012.07.029.png)
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Abstract
En 中文
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. (c) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Pattern recognition
Graphics recognition
Graph clustering
Graph classification
Explicit graph embedding
Fuzzy logic
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