Learning a Metric for Relational Data

Jiajun Pan, Hoel Le Capitaine, Philippe Leray

Abstract: The vast majority of metric learning approaches are dedicated to be applied on data described by feature vectors, with some notable exceptions such as times series and trees or graphs. The objective of this paper is to propose metric learning algorithms that consider (multi)-relational data. The proposed approach takes benefit from both the topological structure of the data and supervised labels.

Conflicts: univ-nantes.fr