Robust L1-norm non-parallel proximal support vector machine. (2nd January 2016)

• Record Type: Journal Article
• Authors: Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang
• Is Part Of: Optimization. Volume 65:Number 1(2016)
• Page Start: 169
• Page End: 183
• Publication Date: 2016-01-02
• Subjects: L1-norm optimization -- non-parallel proximal support vector machine -- robust approximation -- generalized eigenvalues; 90C31 -- 90C59; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2014.994627
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract : In this paper, we propose a robust L1-norm non-parallel proximal support vector machine (L1-NPSVM), which aims at giving a robust performance for binary classification in contrast to GEPSVM, especially for the problem with outliers. There are three mainly properties of the proposed L1-NPSVM. Firstly, different from the traditional GEPSVM which solves two generalized eigenvalue problems, our L1-NPSVM solves a pair of L1-norm optimal problems by using a simple justifiable iterative technique. Secondly, by introducing the L1-norm, our L1-NPSVM is more robust to outliers than GEPSVM to a great extent. Thirdly, compared with GEPSVM, no parameters need to be regularized in our L1-NPSVM. The effectiveness of the proposed method is demonstrated by tests on a simple artificial example as well as on some UCI datasets, which shows the improvements of GEPSVM.