Regularization and variable selection via the elastic net
Regularization and variable selection via the elastic net
复制标题
DOI：
10.1111/j.1467-9868.2005.00503.x
复制
发表时间：
2005-01-01
影响因子：
5.8
通讯作者：
Hastie, T
中科院分区：
文献类型：
--
作者：
Zou, H;Hastie, T
提出了一种新的正则化和变量选择方法--弹性网。真实的世界数据和模拟研究表明，弹性网络往往优于套索，同时享有类似的稀疏表示。此外，弹性网络鼓励分组效应，其中强相关的预测因子倾向于一起进入或离开模型。当预测因子的数量（p）远大于观测值的数量（n）时，弹性网特别有用。相比之下，在p>n的情况下，套索不是一个非常令人满意的变量选择方法。提出了一种名为LARS-EN的算法来有效计算弹性网正则化路径，就像LARS算法对套索的作用一样。
We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p>n case. An algorithm called LARS-EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.