Sparse Distance Weighted Discrimination

Boxiang Wang; Hui Zou

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arXiv · 1501.06066

Sparse Distance Weighted Discrimination

Boxiang Wang·Hui Zou

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Abstract

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.

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Boxiang Wang, Hui Zou. 2015-01-24. Sparse Distance Weighted Discrimination. https://arxiv.org/abs/1501.06066

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