  Distance weighted discrimination (DWD) was originally proposed to handle the
data piling issue in the support vector machine. In this paper, 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. In order 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.
