Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
Machine Learning
2012-07-03 v1 Machine Learning
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
@article{arxiv.1206.6453,
title = {Adaptive Canonical Correlation Analysis Based On Matrix Manifolds},
author = {Florian Yger and Maxime Berar and Gilles Gasso and Alain Rakotomamonjy},
journal= {arXiv preprint arXiv:1206.6453},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)