文章基本信息

标题：Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”
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作者：Liu, Meimei ; Cheng, Guang
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2018
卷号：11
期号：3
页码：423-424
DOI：10.4310/SII.2018.v11.n3.a3
语种：English
出版社：International Press
摘要：DOSK proposed in [2] aims to perform both variable selection and data extraction at the same time under the “finite sparsity” assumption. In this short note, we propose two alternative approaches based on random projection and importance sampling without such an assumption. Furthermore, we compare these two methods with DOSK empirically in terms of statistical accuracy and computing efficiency.
关键词：data extraction; importance sampling; kernel regression; reproducing kernel Hilbert space; random projection; variable selection