Scalable metric learning for co-embedding

3Citations
Citations of this article
22Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

We present a general formulation of metric learning for coembedding, where the goal is to relate objects from different sets. The framework allows metric learning to be applied to a wide range of problems—including link prediction, relation learning, multi-label tagging and ranking—while allowing training to be reformulated as convex optimization. For training we provide a fast iterative algorithm that improves the scalability of existing metric learning approaches. Empirically, we demonstrate that the proposed method converges to a global optimum efficiently, and achieves competitive results in a variety of co-embedding problems such as multi-label classification and multirelational prediction.

Cite

CITATION STYLE

APA

Mirzazadeh, F., White, M., György, A., & Schuurmans, D. (2015). Scalable metric learning for co-embedding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9284, pp. 625–642). Springer Verlag. https://doi.org/10.1007/978-3-319-23528-8_39

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free