Learning a Mahalanobis Metric from Equivalence Constraints
Abstract
Many learning algorithms use a metric defined over the input space as a principal tool, and their performance critically depends on the quality of this metric. We address the problem of learning metrics using side-information in the form of equivalence constraints. Unlike labels, we demonstrate that this type of side-information can sometimes be automatically obtained without the need of human intervention. We show how such side-information can be used to modify the representation of the data, leading to improved clustering and classification.
Cite
Text
Bar-Hillel et al. "Learning a Mahalanobis Metric from Equivalence Constraints." Journal of Machine Learning Research, 2005.Markdown
[Bar-Hillel et al. "Learning a Mahalanobis Metric from Equivalence Constraints." Journal of Machine Learning Research, 2005.](https://mlanthology.org/jmlr/2005/barhillel2005jmlr-learning/)BibTeX
@article{barhillel2005jmlr-learning,
title = {{Learning a Mahalanobis Metric from Equivalence Constraints}},
author = {Bar-Hillel, Aharon and Hertz, Tomer and Shental, Noam and Weinshall, Daphna},
journal = {Journal of Machine Learning Research},
year = {2005},
pages = {937-965},
volume = {6},
url = {https://mlanthology.org/jmlr/2005/barhillel2005jmlr-learning/}
}