Error estimation of weighted nonlocal Laplacian on random point cloud
Numerical Analysis
2018-09-25 v1
Abstract
We analyze the convergence of the weighted nonlocal Laplacian (WNLL) on high dimensional randomly distributed data. The analysis reveals the importance of the scaling weight with and be the number of entire and labeled data, respectively. The result gives a theoretical foundation of WNLL for high dimensional data interpolation.
Cite
@article{arxiv.1809.08622,
title = {Error estimation of weighted nonlocal Laplacian on random point cloud},
author = {Zuoqiang Shi and Bao Wang and Stanley J. Osher},
journal= {arXiv preprint arXiv:1809.08622},
year = {2018}
}
Comments
15 pages; 2 figures