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Sparse linear regression from perturbed data
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DOI：10.1016/j.automatica.2020.109284.png)
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Abstract
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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 l(2) and l(1) minimization problems. Both theoretical guarantees and numerical results are illustrated. (c) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Linear systems
Compressed sensing
Perturbed data
Estimation algorithms
Non-convex optimization
System identification
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