Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78063
Title: Fast algorithms for large-scale generalized distance weighted discrimination
Authors: Lam, XY
Marron, JS
Sun, D 
Toh, KC
Keywords: Convergent multi-block ADMM
Data piling
Support vector machine
Issue Date: 2018
Publisher: Taylor & Francis Inc.
Source: Journal of computational and graphical statistics, 2018, v. 27, no. 2, p. 368-379 How to cite?
Journal: Journal of computational and graphical statistics 
Abstract: High-dimension-low-sample size statistical analysis is important in a wide range of applications. In such situations, the highly appealing discrimination method, support vector machine, can be improved to alleviate data piling at the margin. This leads naturally to the development of distance weighted discrimination (DWD), which can be modeled as a second-order cone programming problem and solved by interior-point methods when the scale (in sample size and feature dimension) of the data is moderate. Here, we design a scalable and robust algorithm for solving large-scale generalized DWD problems. Numerical experiments on real datasets from the UCI repository demonstrate that our algorithm is highly efficient in solving large-scale problems, and sometimes even more efficient than the highly optimized LIBLINEAR and LIBSVM for solving the corresponding SVM problems. Supplementary material for this article is available online.
URI: http://hdl.handle.net/10397/78063
ISSN: 1061-8600
EISSN: 1537-2715
DOI: 10.1080/10618600.2017.1366915
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