A fast tracking algorithm for generalized LARS/LASSO

被引：20
|
作者：
Keerthi, S. Sathiya
Shevade, Shirish
机构：
[1] Yahoo Res, Burbank, CA 91504 USA
[2] Indian Inst Sci, Dept Comp Sci & Automat, Bangalore 560012, Karnataka, India
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 06期
关键词：
generalized Least Angle Regression (LARS); Least Absolute Shrinkage and Selection Operator (LASSO); sparse logistic regression;
D O I：
10.1109/TNN.2007.900229
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This letter gives an efficient algorithm for tracking the solution curve of sparse logistic regression With respect to the L I regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu's path tracking algorithm on the approximate problem, and then applying a correction to get to the true path. Application of the algorithm to text classification and sparse kernel logistic regression shows that the algorithm is efficient.
引用
收藏
页码：1826 / 1830
页数：5
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