Sparse Distance Weighted Discrimination
This work addresses computational bottlenecks in high-dimensional classification for researchers and practitioners using sparse DWD methods, representing an incremental improvement over existing approaches.
The authors tackled the computational challenge of sparse penalized Distance Weighted Discrimination (DWD) for high-dimensional classification by developing an efficient algorithm that computes solution paths across regularization parameters, implemented in an R package called sdwd, with extensive experiments demonstrating improved computational efficiency and classification performance.
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.