Scalable Metric Learning for Co-Embedding
Abstract
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.
Cite
Text
Mirzazadeh et al. "Scalable Metric Learning for Co-Embedding." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23528-8_39Markdown
[Mirzazadeh et al. "Scalable Metric Learning for Co-Embedding." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/mirzazadeh2015ecmlpkdd-scalable/) doi:10.1007/978-3-319-23528-8_39BibTeX
@inproceedings{mirzazadeh2015ecmlpkdd-scalable,
title = {{Scalable Metric Learning for Co-Embedding}},
author = {Mirzazadeh, Farzaneh and White, Martha and György, András and Schuurmans, Dale},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {625-642},
doi = {10.1007/978-3-319-23528-8_39},
url = {https://mlanthology.org/ecmlpkdd/2015/mirzazadeh2015ecmlpkdd-scalable/}
}