Efficient Learning of Mahalanobis Metrics for Ranking
Abstract
We develop an efficient algorithm to learn a Mahalanobis distance metric by directly optimizing a ranking loss. Our approach focuses on optimizing the top of the induced rankings, which is desirable in tasks such as visualization and nearest-neighbor retrieval. We further develop and justify a simple technique to reduce training time significantly with minimal impact on performance. Our proposed method significantly outperforms alternative methods on several real-world tasks, and can scale to large and high-dimensional data.
Cite
Text
Lim and Lanckriet. "Efficient Learning of Mahalanobis Metrics for Ranking." International Conference on Machine Learning, 2014.Markdown
[Lim and Lanckriet. "Efficient Learning of Mahalanobis Metrics for Ranking." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/lim2014icml-efficient/)BibTeX
@inproceedings{lim2014icml-efficient,
title = {{Efficient Learning of Mahalanobis Metrics for Ranking}},
author = {Lim, Daryl and Lanckriet, Gert},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {1980-1988},
volume = {32},
url = {https://mlanthology.org/icml/2014/lim2014icml-efficient/}
}