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A Metric Learning Approach to Graph Edit Costs for Regression

Jia, Linlin; Gaüzère, Benoit; Yger, Florian; Honeine, Paul (2021), A Metric Learning Approach to Graph Edit Costs for Regression, in Torsello, Andrea; Rossi, Luca; Pelillo, Marcello, Structural, Syntactic, and Statistical Pattern Recognition, Springer, p. 238-247. 10.1007/978-3-030-73973-7_23

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21.s+sspr.graphregression.pdf (399.4Kb)
Type
Communication / Conférence
Date
2021
Conference title
Joint IAPR International Workshops, S+SSPR 2020
Conference date
2021-01
Conference city
Padua
Conference country
Italy
Book title
Structural, Syntactic, and Statistical Pattern Recognition
Book author
Torsello, Andrea; Rossi, Luca; Pelillo, Marcello
Publisher
Springer
ISBN
978-3-030-73972-0
Number of pages
378
Pages
238-247
Publication identifier
10.1007/978-3-030-73973-7_23
Metadata
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Author(s)
Jia, Linlin
Gaüzère, Benoit
Yger, Florian
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Honeine, Paul
Abstract (EN)
Graph edit distance (GED) is a widely used dissimilarity measure between graphs. It is a natural metric for comparing graphs and respects the nature of the underlying space, and provides interpretability for operations on graphs. As a key ingredient of the GED, the choice of edit cost functions has a dramatic effect on the GED and therefore the classification or regression performances. In this paper, in the spirit of metric learning, we propose a strategy to optimize edit costs according to a particular prediction task, which avoids the use of predefined costs. An alternate iterative procedure is proposed to preserve the distances in both the underlying spaces, where the update on edit costs obtained by solving a constrained linear problem and a re-computation of the optimal edit paths according to the newly computed costs are performed alternately. Experiments show that regression using the optimized costs yields better performances compared to random or expert costs.
Subjects / Keywords
Graph edit distance; Edit costs; Metric Learning

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