Scalable metric learning for co-embedding
- Farzaneh Mirzazadeh, University of Alberta
- Martha White, University of Alberta
- András György
- Dale Schuurmans, AICML
We present a general formulation of metric learning for co-embedding, 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 multi-relational prediction.
Citation
F. Mirzazadeh, M. White, A. György, D. Schuurmans. "Scalable metric learning for co-embedding". European Conference on Machine Learning (ECML), (ed: Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge A.), pp 625-642, September 2015.| Keywords: | Neural Information Processing System, Link Prediction, Machine Learn Research, Training Objective, Training Problem |
| Category: | In Conference |
| Web Links: | Erratum |
| Springer |
BibTeX
@incollection{Mirzazadeh+al:ECML15,
author = {Farzaneh Mirzazadeh and Martha White and András György and
Dale Schuurmans},
title = {Scalable metric learning for co-embedding},
Editor = {Appice A., Rodrigues P., Santos Costa V., Soares C., Gama J., Jorge
A.},
Pages = {625-642},
booktitle = {European Conference on Machine Learning (ECML)},
year = 2015,
}Last Updated: February 14, 2020Submitted by Sabina P
