论文标题
SparseGl：用于估计稀疏组拉索的R包装
sparsegl: An R Package for Estimating Sparse Group Lasso
论文作者
论文摘要
稀疏组拉索是一种高维回归技术，可用于预测因子具有自然分组结构并在组和个体预测指标水平上鼓励稀疏性的问题。在本文中，我们讨论了一个用于计算此类正则模型的新R软件包。目的是提供高度优化的解决方案例程，以实现非常大的数据集的分析，尤其是在稀疏设计矩阵的背景下。
The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices.
