论文标题
从扰动数据中稀疏线性回归
Sparse linear regression from perturbed data
论文作者
论文摘要
稀疏线性回归的问题与来自大型数据集的线性系统识别的上下文相关。当从实际实验中收集数据时，测量始终会受到扰动或低精度表示的影响。但是，由于其数学复杂性，在文献中几乎没有研究来自完全扰动数据的稀疏线性回归问题。在本文中，我们表明，通过假设有界的扰动，可以通过解决低复合L2和L1最小化问题来解决此问题。理论保证和数值结果都在论文中说明。
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.
