Learning gradients on manifolds
Abstract
A common belief in high-dimensional data analysis is that data are concentrated on a low-dimensional manifold. This motivates simultaneous dimension reduction and regression on manifolds. We provide an algorithm for learning gradients on manifolds for dimension reduction for high-dimensional data with few observations. We obtain generalization error bounds for the gradient estimates and show that the convergence rate depends on the intrinsic dimension of the manifold and not on the dimension of the ambient space. We illustrate the efficacy of this approach empirically on simulated and real data and compare the method to other dimension reduction procedures.
Cite
@article{arxiv.1002.4283,
title = {Learning gradients on manifolds},
author = {Sayan Mukherjee and Qiang Wu and Ding-Xuan Zhou},
journal= {arXiv preprint arXiv:1002.4283},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.3150/09-BEJ206 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)