Robust and sparse support vector machine via hybrid truncated loss for supervised classification

• URL: http://arxiv.org/abs/2606.05814v1
• Date: Thu, 04 Jun 2026 07:53:48 GMT
• Title: Robust and sparse support vector machine via hybrid truncated loss for supervised classification
• Abstract summary: We propose a hybrid truncated loss function ($_Lmathrmht$-SVM) for single-view classification.<n>We show that $L_mathrmht$-SVM achieves higher accuracy with fewer support vectors and better noise than five single-view methods.
• Score: 9.063787748337715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult. Convex losses such as the hinge loss and least-squares loss are sensitive to outliers, while bounded non-convex losses often lead to high computational cost. To address this, we propose a hybrid truncated loss function ($L_{\mathrm{ht}}$) that is both sparse and bounded, and build the $L_{\mathrm{ht}}$-SVM model for single-view classification. We introduce the P-stationary point and use it to establish the first-order necessary and sufficient optimality conditions. Based on these conditions, we design an alternating direction method of multipliers with a working-set strategy that reduces computational cost and achieves global convergence. We further extend $L_{\mathrm{ht}}$-SVM to multi-view learning by adding structural information and view weights, resulting in Mv$L_{\mathrm{ht}}$-SVM, which follows both the consensus and complementarity principles. Experiments on synthetic, real-world, and image datasets show that $L_{\mathrm{ht}}$-SVM achieves higher accuracy with fewer support vectors and better noise robustness than five single-view methods, while Mv$L_{\mathrm{ht}}$-SVM outperforms six multi-view methods in accuracy, precision, recall, and F1-score.

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