Sparse Support Recovery with l∞ Data Fidelity
(2016) International Traveling Workshop on Interactions Between Sparse Models and Technology — Location: Aalborg (24.August.2016)
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- Abstract
- This paper investigates non-uniform guarantees of $ell_1$ minimization, subject to an $ell_infty$ data fidelity constraint, to stably recover the support of a sparse vector when solving noisy linear inverse problems. Our main contribution consists in giving a sufficient condition, framed in terms of the notion of dual certificates, to ensure that a solution of the $ell_1-ell_infty$ convex program has a support containing that of the original vector, when the noise level is sufficiently small.
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Degraux, K., Peyré, G., Fadili, J. M., & Jacques, L. (2016). Sparse Support Recovery with l∞ Data Fidelity. Proceedings of the third “international Traveling Workshop on Interactions between Sparse models and Technology” (iTWIST′16), arXiv:1609.04167. Published. International Traveling Workshop on Interactions Between Sparse Models and Technology, Aalborg. https://hdl.handle.net/2078.5/231352