Robust capped L1-norm twin support vector machine. (June 2019)

• Record Type: Journal Article
• Authors: Wang, Chunyan; Ye, Qiaolin; Luo, Peng; Ye, Ning; Fu, Liyong
• Is Part Of: Neural networks. Volume 114(2019)
• Page Start: 47
• Page End: 59
• Publication Date: 2019-06
• Subjects: Machine learning -- TWSVM -- Capped L1-norm -- Robustness; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.01.016
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers. In this paper, we propose a new robust capped L1-norm twin support vector machine (CTWSVM), which sustains the advantages of TWSVM and promotes the robustness in solving a binary classification problem with outliers. The solution of the proposed method can be achieved by optimizing a pair of capped L1-norm related problems using a newly-designed effective iterative algorithm. Also, we present some theoretical analysis on existence of local optimum and convergence of the algorithm. Extensive experiments on an artificial dataset and several UCI datasets demonstrate the robustness and feasibility of our proposed CTWSVM.