Least absolute shrinkage and selection operator (LASSO) is one of the most commonly used methods for shrinkage estimation and variable selection. Robust variable selection methods via penalized regression, such as least absolute deviation LASSO (LAD-LASSO), etc., have gained growing attention in works of literature. However those penalized regression procedures are still sensitive to noisy data. Furthermore, “concept drift” makes learning from streaming data fundamentally different from the traditional batch learning. Focusing on the shrinkage estimation and variable selection tasks on noisy streaming data, this paper presents a noise-resilient online learning regression model, i.e. canal-LASSO. Comparing with the LASSO and LAD-LASSO, canal-LASSO is resistant to noisy data in both explanatory variables and response variables. Extensive simulation studies demonstrate satisfactory sparseness and noise-resilient performances of canal-LASSO.

中文翻译：

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Canal-LASSO：稀疏的抗噪声的在线线性回归模型

最小绝对收缩和选择算子（LASSO）是收缩估计和变量选择的最常用方法之一。通过惩罚回归的鲁棒变量选择方法，例如最小绝对偏差LASSO（LAD-LASSO）等，在文献研究中受到越来越多的关注。但是，那些惩罚性回归程序仍然对嘈杂的数据敏感。此外，“概念漂移”使得从流数据中进行学习与传统的批处理学习根本不同。针对噪声数据流的收缩估计和变量选择任务，本文提出了一种抗噪声的在线学习回归模型，即canal-LASSO。与LASSO和LAD-LASSO相比，运河LASSO可以抵抗解释变量和响应变量中的噪声数据。