2015/09/26 by Jun Zhang, Urbashi Mitra, Zhang, Jun +6
Computer Science · Engineering · Mathematics · #Blind Source Separation Techniques #Electrical and Bioimpedance Tomography #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1509.07947
6 pages, 6 figures
arxiv created 2015/09/26 · openalex publication_date 2015/09/26 · arxiv updated 2015/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper study recovery conditions of weighted L1 minimization for signal reconstruction from compressed sensing measurements. A sufficient condition for exact recovery by using the general weighted L1 minimization is derived, which builds a direct relationship between the weights and the recoverability. Simulation results indicates that this sufficient condition provides a precise prediction of the scaling law for the weighted L1 minimization.