2019/09/09 by V. Cerone, Sophie M. Fosson, Cerone, Vito +3
Engineering · Medicine · #Advanced MRI Techniques and Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1909.03705
openalex publication_date 2019/09/09 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
We consider the problem of the recovery of a k-sparse vector from compressed\nlinear measurements when data are corrupted by a quantization noise. When the\nnumber of measurements is not sufficiently large, different k-sparse\nsolutions may be present in the feasible set, and the classical l1 approach may\nbe unsuccessful. For this motivation, we propose a non-convex quadratic\nprogramming method, which exploits prior information on the magnitude of the\nnon-zero parameters. This results in a more efficient support recovery. We\nprovide sufficient conditions for successful recovery and numerical simulations\nto illustrate the practical feasibility of the proposed method.\n