Abstract
This paper presents an on-line learning method to automatically deduce the insertion, deletion and substitution costs of the graph edit distance, which is inspired in a previously published off-line learning method. The original method is based on embedding the ground-truth node-to-node mappings into a Euclidean space and learning the edit costs through the hyper-plane in this new space that splits the nodes into the mapped ones and the non-mapped ones. The new method has the advantage of learning the edit costs and computing the graph edit distance can be done simultaneously. Experimental validation shows that the matching accuracy is competitive with the off-line method but without the need of the whole learning set.
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Acknowledgements
This research is supported by projects TIN2016-77836-C2-1-R, DPI2016-78957-, H2020-ICT-2014-1-644271, H2020-NMBP-TO-IND-2018-814426.
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Rica, E., Álvarez, S., Serratosa, F. (2019). On-Line Learning the Edit Costs Based on an Embedded Model. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_12
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