Skip to Main content Skip to Navigation
Conference papers

Relational Constraints for Metric Learning on Relational Data

Jiajun Pan 1, 2 Hoel Le Capitaine 1, 2 Philippe Leray 1, 2 
2 DUKe - Data User Knowledge
LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : Most of metric learning approaches are dedicated to be applied on data described by feature vectors, with some notable exceptions such as times series, trees or graphs. The objective of this paper is to propose a metric learning algorithm that specifically considers relational data. The proposed approach can take benefit from both the topological structure of the data and supervised labels. For selecting relative constraints representing the relational information, we introduce a link-strength function that measures the strength of relationship links between entities by the side-information of their common parents. We show the performance of the proposed method with two different classical metric learning algorithms, which are ITML (Information Theoretic Metric Learning) and LSML (Least Squares Metric Learning), and test on several real-world datasets. Experimental results show that using relational information improves the quality of the learned metric.
Complete list of metadata

Cited literature [9 references]  Display  Hide  Download
Contributor : Hoel Le Capitaine Connect in order to contact the contributor
Submitted on : Wednesday, February 13, 2019 - 9:51:24 AM
Last modification on : Friday, August 5, 2022 - 2:54:51 PM
Long-term archiving on: : Tuesday, May 14, 2019 - 6:13:09 PM


Files produced by the author(s)


  • HAL Id : hal-02017253, version 1


Jiajun Pan, Hoel Le Capitaine, Philippe Leray. Relational Constraints for Metric Learning on Relational Data. Eighth International Workshop on Statistical Relational AI, IJCAI, Jul 2018, Stockholm, Sweden. ⟨hal-02017253⟩



Record views


Files downloads