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Robust adaptive learning framework for semi-supervised pattern classification
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AI
DOI：10.1016/j.sigpro.2024.109594.png)
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Abstract
En 中文
Hessian scatter regularized twin support vector machine (HSR-TSVM) employs hinge loss function and L-2- 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 semisupervised classification tasks. In CL2,pHSR-TSVM, the generalized adaptive robust loss function L-delta(u) is first innovatively introduced to overcome the problem that hinge loss function is not sensitive to noise and outliers. Intuitively, L-delta(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 delta. Secondly, the robust distance metric capped L-2,L-p-norm is introduced in CL2,p HSR-TSVM to reduce and eliminate the exaggerated influence of L-2-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.
Keywords:
Non-convex distance metric
Semi-supervised robust classification
Generalized adaptive robust loss function
Outliers
Kernel method