Graph connection Laplacian methods can be made robust to noise

Noureddine El Karoui, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)346-372
Number of pages27
JournalAnnals of Statistics
Issue number1
StatePublished - Feb 1 2016


  • Concentration of measure
  • Graph connection Laplacian
  • Kernel methods
  • Random matrices
  • Spectral geometry
  • Vector diffusion maps

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Graph connection Laplacian methods can be made robust to noise'. Together they form a unique fingerprint.

Cite this