TY - BOOK
T1 - Graph embedding through probabilistic graphical model applied to symbolic graphs
AU - Jarraya, Hana
AU - Terrades, Oriol Ramos
AU - Lladós, Josep
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Attributed graph
KW - Graph embedding
KW - Probabilistic graphical model
KW - Structured support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85021239509&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58838-4_43
DO - 10.1007/978-3-319-58838-4_43
M3 - Proceeding
AN - SCOPUS:85021239509
SN - 9783319588377
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
BT - Graph embedding through probabilistic graphical model applied to symbolic graphs
ER -