{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/impulsive-noise-robust-sparse-recovery-via","title":"Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm","arxiv_id":"1804.04614","date":"2018-04-12","proceeding":null,"authors":["Amirhossein Javaheri","Hadi Zayyani","Mario A. T. Figueiredo","Farrokh Marvasti"],"abstract":"This paper investigates the problem of sparse signal recovery in the presence\nof additive impulsive noise. The heavytailed impulsive noise is well modelled\nwith stable distributions. Since there is no explicit formulation for the\nprobability density function of $S\\alpha S$ distribution, alternative\napproximations like Generalized Gaussian Distribution (GGD) are used which\nimpose $\\ell_p$-norm fidelity on the residual error. In this paper, we exploit\na Continuous Mixed Norm (CMN) for robust sparse recovery instead of\n$\\ell_p$-norm. We show that in blind conditions, i.e., in case where the\nparameters of noise distribution are unknown, incorporating CMN can lead to\nnear optimal recovery. We apply Alternating Direction Method of Multipliers\n(ADMM) for solving the problem induced by utilizing CMN for robust sparse\nrecovery. In this approach, CMN is replaced with a surrogate function and\nMajorization-Minimization technique is incorporated to solve the problem.\nSimulation results confirm the efficiency of the proposed method compared to\nsome recent algorithms in the literature for impulsive noise robust sparse\nrecovery.","url_abs":"http://arxiv.org/abs/1804.04614v1","url_pdf":"http://arxiv.org/pdf/1804.04614v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"impulsive-noise-robust-sparse-recovery-via","repo_url":"https://github.com/FWen/Lp-Robust-CS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}