TY - BOOK
T1 - Learning Structural Loss Parameters on Graph Embedding Applied on Symbolic Graphs
AU - Jarraya, Hana
AU - Terrades, Oriol Ramos
AU - Llados, Josep
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/25
Y1 - 2018/1/25
N2 - We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1-slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
AB - We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1-slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
KW - Attributed Graph
KW - Graph Embedding
KW - Probabilistic Graphical Model
KW - Structural dissimilarity distance
KW - Structured Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=85045234116&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.268
DO - 10.1109/ICDAR.2017.268
M3 - Proceeding
AN - SCOPUS:85045234116
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
BT - Learning Structural Loss Parameters on Graph Embedding Applied on Symbolic Graphs
ER -