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Adaptive ridge procedure for L0-penalized weighted support vector machines
Journal of the Korean Data & Information Science Society 2017;28:1271-8
Published online November 30, 2017
© 2017 Korean Data & Information Science Society.

Kyoung Hee Kim1 · Seung Jun Shin2

1Department of Statistics, Sungshin Women's University
2Department of Statistics, Korea University
Correspondence to: Seung Jun Shin
Assistant professor, Department of Statistics, Korea University, Seoul 712-749, Korea. E-mail: sjshin@korea.ac.kr
Received September 29, 2017; Revised October 30, 2017; Accepted November 1, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Although the L0-penalty is the most natural choice to identify the sparsity structure of the model, it has not been widely used due to the computational bottleneck. Recently, the adaptive ridge procedure is developed to efficiently approximate a Lq-penalized problem to an iterative L2-penalized one. In this article, we proposed to apply the adaptive ridge procedure to solve the L0-penalized weighted support vector machine (WSVM) to facilitate the corresponding optimization. Our numerical investigation shows the advantageous performance of the L0-penalized WSVM compared to the conventional WSVM with L2 penalty for both simulated and real data sets.
Keywords : L0-penalty, support vector machines, variable selection.