Abstract
<p style='text-indent:20px;'>Projection twin support vector machine (PTSVM) is an effective tool for classification. However, it is sensitive to outliers or the noise due to the utilization of the squared L2-norm distance. To alleviate the sensitivity to outliers or the noise, we propose a capped L1-norm projection twin support vector machine (CPTSVM), where the L2-norm distance is replaced by the capped L1-norm to confer the robustness to classifiers. CPTSVM is formulated as a pair of non-convex and non-smooth SVM-type problems. To solve these difficult problems, we present an iterative algorithm for CPTSVM as well as its convergence properties. Numerical experiments on artificial and benchmark datasets demonstrate the robustness and feasibility of our proposed method.</p>
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