The Annals of Statistics
- Ann. Statist.
- Volume 44, Number 1 (2016), 346-372.
Graph connection Laplacian methods can be made robust to noise
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.
Ann. Statist., Volume 44, Number 1 (2016), 346-372.
Received: May 2014
Revised: September 2014
First available in Project Euclid: 5 January 2016
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El Karoui, Noureddine; Wu, Hau-Tieng. Graph connection Laplacian methods can be made robust to noise. Ann. Statist. 44 (2016), no. 1, 346--372. doi:10.1214/14-AOS1275. https://projecteuclid.org/euclid.aos/1452004789
- Supplement to “Graph connection Laplacian methods can be made robust to noise”. We provide detailed proofs and supplementary information in the Supplementary Material.