Smooth graph signal recovery via efficient Laplacian solvers

Gita Babazadeh Eslamlou; Alexander Jung; Norbert Goertz

ICASSP 2017accepted0 citations

Smooth graph signal recovery via efficient Laplacian solvers

Gita Babazadeh Eslamlou, Alexander Jung, Norbert Goertz

Abstract

We consider the problem of recovering a smooth graph signal from noisy samples observed at a small number of nodes. The signal recovery is formulated as a convex optimization problem using Tikhonov regularization based on the graph Laplacian quadratic form. The optimality conditions for this optimization problem form a system of linear equations involving the graph Laplacian. We solve this linear system via the iterative Gauss-Seidel method, which is shown to be particularly well-suited for smooth graph signal recovery. The effectiveness of the proposed recovery method is verified by numerical experiments using a real-world data-set.

BibTeX

@inproceedings{icassp2017_smoothgraphsigna,
  title = {Smooth graph signal recovery via efficient Laplacian solvers},
  author = {Gita Babazadeh Eslamlou and Alexander Jung and Norbert Goertz},
  booktitle = {ICASSP 2017},
  year = {2017}
}