Regularization Paths for Generalized Linear Models via Coordinate Descent
Regularization Paths for Generalized Linear Models via Coordinate Descent
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DOI：
10.18637/jss.v033.i01
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发表时间：
2010-02-01
影响因子：
5.8
通讯作者：
Tibshirani, Rob
中科院分区：
文献类型：
--
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Rob
我们开发了具有凸惩罚的广义线性模型的快速估计算法。模型包括线性回归、两类逻辑回归和多项回归问题，惩罚包括l(1)（套索）、l(2)（岭回归）和两者的混合（弹性网）。该算法使用沿正则化路径计算的周期性坐标下降。该方法既能处理大问题，又能有效地处理稀疏特征。在比较时间上，我们发现新算法比竞争方法要快得多。
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include l(1) (the lasso), l(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.