Convergence of Laplacian Eigenmaps
Abstract
Geometrically based methods for various tasks of machine learning have attracted considerable attention over the last few years. In this paper we show convergence of eigenvectors of the point cloud Laplacian to the eigen- functions of the Laplace-Beltrami operator on the underlying manifold, thus establishing the first convergence results for a spectral dimensionality re- duction algorithm in the manifold setting.
Cite
Text
Belkin and Niyogi. "Convergence of Laplacian Eigenmaps." Neural Information Processing Systems, 2006.Markdown
[Belkin and Niyogi. "Convergence of Laplacian Eigenmaps." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/belkin2006neurips-convergence/)BibTeX
@inproceedings{belkin2006neurips-convergence,
title = {{Convergence of Laplacian Eigenmaps}},
author = {Belkin, Mikhail and Niyogi, Partha},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {129-136},
url = {https://mlanthology.org/neurips/2006/belkin2006neurips-convergence/}
}