Robust adaptive learning framework for semi-supervised pattern classification

• Published in: Signal processing 2024-11, Vol.224, p.109594, Article 109594
• Main Authors: Ma, Jun, Yu, Guolin
• Format: Article
• Language: English
• Subjects: Generalized adaptive robust loss function; Kernel method; Non-convex distance metric; Outliers; Semi-supervised robust classification
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2024.109594

Hessian scatter regularized twin support vector machine (HSR-TSVM) employs hinge loss function and L2-distance metric, which makes it ineffective in dealing with outliers and noise data problems. Aiming to this problem, this paper a novel robust adaptive learning framework CL2,pHSR-TSVM is developed for semi-supervised classification tasks. In CL2,pHSR-TSVM, the generalized adaptive robust loss function Lδ(u) is first innovatively introduced to overcome the problem that hinge loss function is not sensitive to noise and outliers. Intuitively, Lδ(u) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter δ. Secondly, the robust distance metric capped L2,p-norm is introduced in CL2,pHSR-TSVM to reduce and eliminate the exaggerated influence of L2-distance metric on the learning process of outliers, especially when the outliers are far from the normal data distribution, by setting the appropriate parameters. Furthermore, to improve the computational efficiency of CL2,pHSR-TSVM, the fast CL2,pHSR-TSVM is presented for semi-supervised classification tasks. Finally, two effective algorithms are designed to solve our methods respectively, and the convergence and computational complexity are analyzed theoretically. Experimental results demonstrate the effectiveness and robustness of our methods. •A new robust adaptive learning framework CL2,pHSR-TSVM is presented.•An efficient iterative algorithm is constructed to solve CL2,pHSR-TSVM.•The fast CL2,pHSR-TSVM (FCL2,pHSR-TSVM) is proposed.•The conjugate gradient method is used to solve FCL2,pHSR-TSVM.•The computational complexity and convergence are analyzed and discussed.