Adaptive Canonical Correlation Analysis Based on Matrix Manifolds
Abstract
In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change detection problem in EEG signals.
Cite
Text
Yger et al. "Adaptive Canonical Correlation Analysis Based on Matrix Manifolds." International Conference on Machine Learning, 2012.Markdown
[Yger et al. "Adaptive Canonical Correlation Analysis Based on Matrix Manifolds." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/yger2012icml-adaptive/)BibTeX
@inproceedings{yger2012icml-adaptive,
title = {{Adaptive Canonical Correlation Analysis Based on Matrix Manifolds}},
author = {Yger, Florian and Berar, Maxime and Gasso, Gilles and Rakotomamonjy, Alain},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/yger2012icml-adaptive/}
}