Cellwise contamination remains a challenging problem for data scientists, particularly in research fields that require the selection of sparse features. Traditional robust methods may not be feasible nor efficient in dealing with such contaminated datasets. We propose CR-Lasso, a robust Lasso-type cellwise regularization procedure that performs feature selection in the presence of cellwise outliers by minimising a regression loss and cell deviation measure simultaneously. To evaluate the approach, we conduct empirical studies comparing its selection and prediction performance with several sparse regression methods. We show that CR-Lasso is competitive under the settings considered. We illustrate the effectiveness of the proposed method on real data through an analysis of a bone mineral density dataset.

翻译：细胞级污染对于数据科学家仍然是一个具有挑战性的问题，尤其是在需要稀疏特征选择的研究领域中。传统的鲁棒方法在处理此类受污染数据集时可能不可行或效率低下。我们提出了CR-Lasso，一种鲁棒的Lasso型细胞级正则化流程，通过同时最小化回归损失和细胞偏差度量，在存在细胞级异常值的情况下进行特征选择。为评估该方法，我们进行了实证研究，将其在多个稀疏回归方法中的选择和预测性能进行比较。结果表明，CR-Lasso在考虑的设置下具有竞争力。我们通过对骨密度数据集的分析，展示了所提方法在实际数据中的有效性。