Principal symmetric space analysis
Statistics Theory
2019-08-14 v1 Differential Geometry
Statistics Theory
Abstract
We develop a novel analogue of Euclidean PCA (principal component analysis) for data taking values on a Riemannian symmetric space, using totally geodesic submanifolds as approximating lower dimnsional submanifolds. We illustrate the technique on n-spheres, Grassmannians, n-tori and polyspheres.
Cite
@article{arxiv.1908.04553,
title = {Principal symmetric space analysis},
author = {Stephen R Marsland and Robert I McLachlan and Charles Curry},
journal= {arXiv preprint arXiv:1908.04553},
year = {2019}
}