Abstract
We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
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Acknowledgment
This work has been partially supported by the Spanish project TIN2015-70924-C2-2-R and the CERCA Programme/Generalitat de Catalunya.
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Jarraya, H., Ramos Terrades, O., Lladós, J. (2017). Graph Embedding Through Probabilistic Graphical Model Applied to Symbolic Graphs. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_43
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DOI: https://doi.org/10.1007/978-3-319-58838-4_43
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