【论文翻译】Elastic Net 弹性网

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收藏 2016-03-24

自己翻译的论文Regularization and variable selection via the elastic net，渣翻。虽然翻译的不好, 但是也是我辛辛苦苦熬了几天夜翻译出来的, 希望大家自己下载自己看就好了, 不要上传或转载到其他网站上, 保护知识版权.文档编译由XeLaTex/TexLive，模板由BMC期刊。

这篇Hui Zou和Hastie提出了Elastic Net，可看为Lasso的拓展形式。

Title

Regularization and variable selection via the elastic net

Authors

Hui Zou and Trevor Hastie

Journal

JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B

Year

2005

杂志影响因子

2014年度	2013年度	2012年度	2011年度
3.515	5.721	4.81	3.645

中科院分区

大类：数学, 1区 (Top)
小类：统计学与概率论, 1区

摘要

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.
我们提出了弹性网, 这是一个新的正则化和变量选择方法. 无论是真实数据还是数值模拟都表明弹性网比lasso 的效果好, 同时具有类似的稀疏性质. 而且,弹性网还有群组效应, 即强烈相关的预测变量趋于同时进入或退出模型. 弹性网在预测变量比观测量很大的时候很有用(p ≫ n). 相反, lasso 在p ≫ n 的情形中并不十分适用. 本文还提出了一个很效率的算法来计算弹性网的正则化路径, 称为LARS-EN. 它很像解决lasso 的LARS 算法.

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