Relational Constraints for Metric Learning on Relational Data

TL;DR

This paper introduces a novel metric learning algorithm tailored for relational data, leveraging both topological structure and labels, and demonstrates improved performance on real-world datasets.

Contribution

It proposes a new approach that incorporates relational constraints into metric learning, utilizing a link-strength function to capture relationship importance.

Findings

Relational information enhances metric learning quality.

The method improves results on multiple real-world datasets.

Using topological structure benefits metric accuracy.

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.