Graph connection Laplacian methods can be made robust to noise

Published

Journal Article

© Institute of Mathematical Statistics, 2016. Recently, several data analytic techniques based on graph connection Laplacian (GCL) ideas have appeared in the literature. At this point, the properties of these methods are starting to be understood in the setting where the data is observed without noise. We study the impact of additive noise on these methods and show that they are remarkably robust. As a by-product of our analysis, we propose modifications of the standard algorithms that increase their robustness to noise. We illustrate our results in numerical simulations.

Full Text

Duke Authors

Cited Authors

  • El Karoui, N; Wu, HT

Published Date

  • February 1, 2016

Published In

Volume / Issue

  • 44 / 1

Start / End Page

  • 346 - 372

International Standard Serial Number (ISSN)

  • 0090-5364

Digital Object Identifier (DOI)

  • 10.1214/14-AOS1275

Citation Source

  • Scopus