A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

被引：0

作者：

Yuan, Guo-Xun ^{[1
]}

Chang, Kai-Wei ^{[1
]}

Hsieh, Cho-Jui ^{[1
]}

Lin, Chih-Jen ^{[1
]}

机构：

[1] Natl Taiwan Univ, Dept Comp Sci, Taipei 106, Taiwan

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2010年 / 11卷

关键词：

L1; regularization; linear classification; optimization methods; logistic regression; support vector machines; document classification; NEWTON METHOD; LOGISTIC-REGRESSION; FEATURE-SELECTION; ALGORITHM; DESCENT; GRADIENT; PROJECTION; LASSO;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Large-scale linear classification is widely used in many areas. The L1-regularized form can be applied for feature selection; however, its non-differentiability causes more difficulties in training. Although various optimization methods have been proposed in recent years, these have not yet been compared suitably. In this paper, we first broadly review existing methods. Then, we discuss state-of-the-art software packages in detail and propose two efficient implementations. Extensive comparisons indicate that carefully implemented coordinate descent methods are very suitable for training large document data.

引用

页码：3183 / 3234

页数：52