2020/03/31 by Sophie M. Fosson, S. M. Fosson, Fosson, S. M. +4 · 1 citation
Computer Science · Engineering · Mathematics · #Algorithm #Applied mathematics #Bounded function #Computer science #Context (archaeology) #Control Systems and Identification #FOS: Electrical engineering #FOS: Mathematics #Geology #Identification (biology) #Linear regression #Linear system #Machine learning #Mathematical optimization #Mathematics #Minification #Optimization and Control (math.OC) #Regression #Sparse and Compressive Sensing Techniques #Sparse approximation #Statistics #Structural Health Monitoring Techniques #Systems and Control (eess.SY) #cs.SY #eess.SY #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.2003.14389
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/03/31 · openalex publication_date 2020/03/31 · arxiv updated 2020/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The problem of sparse linear regression is relevant in the context of linear system identification from large datasets. When data are collected from real-world experiments, measurements are always affected by perturbations or low-precision representations. However, the problem of sparse linear regression from fully-perturbed data is scarcely studied in the literature, due to its mathematical complexity. In this paper, we show that, by assuming bounded perturbations, this problem can be tackled by solving low-complex l2 and l1 minimization problems. Both theoretical guarantees and numerical results are illustrated in the paper.