Regularized linear models, such as Lasso, have attracted great attention in statistical learning and data science. However, there is sporadic work on constructing efficient data collection for regularized linear models. In this work, we propose an experimental design approach, using nearly orthogonal Latin hypercube designs, to enhance the variable selection accuracy of the regularized linear models. Systematic methods for constructing such designs are presented. The effectiveness of the proposed method is illustrated with several examples.

翻译：Lasso等正规化线性模型在统计学习和数据科学方面引起了极大关注,然而,在为正规化线性模型构建高效数据收集方面,却有零星的工作。在这项工作中,我们提议采用实验设计方法,使用近正方形的拉丁超立方体设计,提高正规化线性模型的可变选择准确度。提出了构建此类设计的系统方法。用几个例子说明了拟议方法的有效性。