Sparse Distance Weighted Discrimination

Boxiang Wang, Hui Zou

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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 languageEnglish (US)
Pages (from-to)826-838
Number of pages13
JournalJournal of Computational and Graphical Statistics
Issue number3
StatePublished - Jul 2 2016

Bibliographical note

Publisher Copyright:
© 2016 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.


  • DWD
  • High-dimensional classification
  • SVM


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