Joint Dimensionality Reduction and Metric Learning: A Geometric Take
Abstract
To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while we directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms.
Cite
Text
Harandi et al. "Joint Dimensionality Reduction and Metric Learning: A Geometric Take." International Conference on Machine Learning, 2017.Markdown
[Harandi et al. "Joint Dimensionality Reduction and Metric Learning: A Geometric Take." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/harandi2017icml-joint/)BibTeX
@inproceedings{harandi2017icml-joint,
title = {{Joint Dimensionality Reduction and Metric Learning: A Geometric Take}},
author = {Harandi, Mehrtash and Salzmann, Mathieu and Hartley, Richard},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {1404-1413},
volume = {70},
url = {https://mlanthology.org/icml/2017/harandi2017icml-joint/}
}