Friedman Jerome, Hastie Trevor, Tibshirani Rob
Department of Statistics, Stanford University.
J Stat Softw. 2010;33(1):1-22.
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.
我们开发了用于估计具有凸惩罚项的广义线性模型的快速算法。这些模型包括线性回归、二类逻辑回归和多项回归问题，而惩罚项包括ℓ(1)（套索）、ℓ(2)（岭回归）以及两者的混合（弹性网络）。这些算法使用沿正则化路径计算的循环坐标下降法。这些方法可以处理大规模问题，并且还能有效地处理稀疏特征。在比较计时中，我们发现新算法比竞争方法快得多。
