A Sparse-Group Lasso
A Sparse-Group Lasso
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DOI：
10.1080/10618600.2012.681250
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发表时间：
2013-06-01
影响因子：
2.4
通讯作者：
Tibshirani, Robert
中科院分区：
文献类型：
--
作者：
Simon, Noah;Friedman, Jerome;Tibshirani, Robert
对于高维监督学习问题，经常使用特定于问题的假设可以导致更高的精度。对于协变量分组的问题，我们引入了一个带L(1)和L(2)惩罚的正则化线性回归模型。我们讨论了该模型的最优拟合的稀疏性和其他正则化性质，并证明了它具有组内稀疏性和组内稀疏性的理想效果。提出了一种基于加速广义梯度下降法的模型拟合算法，并将该模型和算法推广到凸损失函数。并在模拟数据上验证了模型的有效性和算法的有效性。这篇文章有在线补充材料。
For high-dimensional supervised learning problems, often using problem-specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effects both on a group and within group level, we introduce a regularized model for linear regression with l(1) and l(2) penalties. We discuss the sparsity and other regularization properties of the optimal fit for this model, and show that it has the desired effect of group-wise and within group sparsity. We propose an algorithm to fit the model via accelerated generalized gradient descent, and extend this model and algorithm to convex loss functions. We also demonstrate the efficacy of our model and the efficiency of our algorithm on simulated data. This article has online supplementary material.