Connection graph Laplacian methods can be made robust to noise
This work addresses noise robustness in CGL methods, which is an incremental improvement for data analysis applications.
The paper investigates the robustness of connection graph Laplacian (CGL) methods to additive noise, showing they are remarkably robust and proposing modifications to enhance this robustness, with results illustrated through numerical simulations.
Recently, several data analytic techniques based on connection graph laplacian (CGL) 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.