Learning Distance Metrics for Multi-Label Classification
Abstract
Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder that can transform instances into a feature space where Euclidean distance provides an estimate of the Jaccard distance between the corresponding label vectors. In addition to a linear Mahalanobis style metric, we also present a nonlinear extension that provides a substantial boost in performance. We show that this technique significantly improves upon current approaches for instance based multi-label classification, and also enables interesting data visualisations.
Cite
Text
Gouk et al. "Learning Distance Metrics for Multi-Label Classification." Proceedings of The 8th Asian Conference on Machine Learning, 2016.Markdown
[Gouk et al. "Learning Distance Metrics for Multi-Label Classification." Proceedings of The 8th Asian Conference on Machine Learning, 2016.](https://mlanthology.org/acml/2016/gouk2016acml-learning/)BibTeX
@inproceedings{gouk2016acml-learning,
title = {{Learning Distance Metrics for Multi-Label Classification}},
author = {Gouk, Henry and Pfahringer, Bernhard and Cree, Michael},
booktitle = {Proceedings of The 8th Asian Conference on Machine Learning},
year = {2016},
pages = {318-333},
volume = {63},
url = {https://mlanthology.org/acml/2016/gouk2016acml-learning/}
}