Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations
Abstract
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring when linearizing the manifold prior to performing statistical operations. Using recent advances in manifold computations, we present a comparison between the non-linear analog of Principal Component Analysis, Principal Geodesic Analysis, in its linearized form and its exact counterpart that uses true intrinsic distances. We give examples of datasets for which the linearized version provides good approximations and for which it does not. Indicators for the differences between the two versions are then developed and applied to two examples of manifold valued data: outlines of vertebrae from a study of vertebral fractures and spacial coordinates of human skeleton end-effectors acquired using a stereo camera and tracking software.
Cite
Text
Sommer et al. "Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15567-3_4Markdown
[Sommer et al. "Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/sommer2010eccv-manifold/) doi:10.1007/978-3-642-15567-3_4BibTeX
@inproceedings{sommer2010eccv-manifold,
title = {{Manifold Valued Statistics, Exact Principal Geodesic Analysis and the Effect of Linear Approximations}},
author = {Sommer, Stefan and Lauze, François and Hauberg, Søren and Nielsen, Mads},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {43-56},
doi = {10.1007/978-3-642-15567-3_4},
url = {https://mlanthology.org/eccv/2010/sommer2010eccv-manifold/}
}