2016/04/19 by Xin Yee Lam, J. S. Marron, Lam, Xin Yee +5 · 1 citation
Computer Science · Engineering · Mathematics · #90C06 #90C25 #90C90 #Advanced Statistical Methods and Models #FOS: Mathematics #Face and Expression Recognition #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #math.OC #msc:90C06 #msc:90C25 #msc:90C90
paper · pdf · doi:10.48550/arxiv.1604.05473
openalex publication_date 2016/04/19 · arxiv created 2017/08/17 · arxiv updated 2017/08/18 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
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 data sets 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.