文章基本信息

标题：Sparse Group Lassoのための高速なBlock Coordinate Descent
本地全文：下载
作者：井田安俊 ; 藤原靖宏 ; 鹿島久嗣等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2021
卷号：36
期号：1
页码：1-11
DOI：10.1527/tjsai.36-1_A-JB1
出版社：The Japanese Society for Artificial Intelligence
摘要：Sparse Group Lasso is a method of linear regression analysis that finds sparse parameters in terms of both feature groups and individual features. Block Coordinate Descent is a standard approach to obtain the parameters of Sparse Group Lasso, and iteratively updates the parameters for each parameter group. However, as an update of only one parameter group depends on all the parameter groups or data points, the computation cost is high when the number of the parameters or data points is large. This paper proposes a fast Block Coordinate Descent for Sparse Group Lasso. It efficiently skips the updates of the groups whose parameters must be zeros by using the parameters in one group. In addition, it preferentially updates parameters in a candidate group set, which contains groups whose parameters must not be zeros. Theoretically, our approach guarantees the same results as the original Block Coordinate Descent. Experiments show that our method enhances the efficiency of original method and achieves the same prediction error.
关键词：sparse group lasso;block coordinate descnent;feature selection;sparsity-inducing regularization