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.
|Original language||English (US)|
|Number of pages||13|
|Journal||Journal of Computational and Graphical Statistics|
|State||Published - Jul 2 2016|
Bibliographical notePublisher Copyright:
© 2016 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
- High-dimensional classification