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.
| Original language | English |
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| Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings |
| Publisher | IEEE |
| Pages | 5915-5919 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509041176 |
| DOIs | |
| Publication status | Published - 16 Jun 2017 |
| MoE publication type | A4 Conference publication |
| Event | IEEE International Conference on Acoustics, Speech, and Signal Processing - New Orleans, United States Duration: 5 Mar 2017 → 9 Mar 2017 |
Conference
| Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing |
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| Abbreviated title | ICASSP |
| Country/Territory | United States |
| City | New Orleans |
| Period | 05/03/2017 → 09/03/2017 |
Keywords
- compressed sensing
- graph signal processing
- Laplacian solvers
- Tikhonov-regularization