Sparse Distance Weighted Discrimination
Sparse Distance Weighted Discrimination
复制标题
DOI：
10.1080/10618600.2015.1049700
复制
发表时间：
2016-09-01
影响因子：
2.4
通讯作者：
Zou, Hui
中科院分区：
文献类型：
--
作者：
Wang, Boxiang;Zou, Hui
距离加权辨别（DWD）最初是为了解决支持向量机中的数据堆积问题而提出的。在本文中，我们考虑用于高维分类的稀疏惩罚DWD。目前解决标准DWD的最先进算法是基于二阶锥规划的，但是这种算法对于具有高维数据的稀疏惩罚DWD并不适用。为了克服具有挑战性的计算困难，我们开发了一种非常有效的算法来计算稀疏DWD在给定正则化参数的精细网格下的解路径。我们在一个公开可用的R包sdwd中实现了该算法。我们进行了大量的数值实验来证明我们的方法的计算效率和分类性能。
Distance weighted discrimination (DWD) was originally proposed to handle the data piling issue in the support vector machine. In this article, we consider the sparse penalized DWD for high-dimensional classification. The state-of-the-art algorithm for solving the standard DWD is based on second-order cone programming, however such an algorithm does not work well for the sparse penalized DWD with high-dimensional data. To overcome the challenging computation difficulty, we develop a very efficient algorithm to compute the solution path of the sparse DWD at a given fine grid of regularization parameters. We implement the algorithm in a publicly available R package sdwd. We conduct extensive numerical experiments to demonstrate the computational efficiency and classification performance of our method.