Non-Parametric Regression Between Manifolds
Abstract
This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, computer vision, over robotics to computer graphics. We present a new algorithmic scheme for the solution of this general learning problem based on regularized empirical risk minimization. The regularization functional takes into account the geometry of input and output manifold, and we show that it implements a prior which is particularly natural. Moreover, we demonstrate that our algorithm performs well in a difficult surface registration problem.
Cite
Text
Steinke and Hein. "Non-Parametric Regression Between Manifolds." Neural Information Processing Systems, 2008.Markdown
[Steinke and Hein. "Non-Parametric Regression Between Manifolds." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/steinke2008neurips-nonparametric/)BibTeX
@inproceedings{steinke2008neurips-nonparametric,
title = {{Non-Parametric Regression Between Manifolds}},
author = {Steinke, Florian and Hein, Matthias},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {1561-1568},
url = {https://mlanthology.org/neurips/2008/steinke2008neurips-nonparametric/}
}