基于动量加速零阶减小方差的鲁棒支持向量机

Robust Support Vector Machine Based on Momentum Acceleration Zero-Order Variance Reduction

在实际分类问题中,由于人为或其他因素的影响,数据中往往存在一定的噪声,而传统支持向量机(SVM)使用的铰链损失函数对噪声数据敏感,且分类性能较差。为消除噪声数据的影响,提出一种新的鲁棒SVM算法。通过引入新形式的损失函数,并基于间隔分布的思想,建立鲁棒SVM优化模型提高SVM的抗噪性,运用零阶减小方差算法并结合动量加速技术,给出一种新的优化模型求解方法。实验结果表明,该方法通过引入梯度修正项降低了方差对算法的影响,同时结合动量加速技术,明显提高了算法的收敛速度。

In the actual classification problem,there is often a certain amount of noise in the data caused by the influence of artificial or other factors,so it is very important to improve the anti-noise ability of the classifier.However,the hinge loss function used by the traditional Support Vector Machine(SVM)is sensitive to noisy data and has poor classification performance.In order to eliminate the influence of noisy data,this paper proposes a robust SVM based on momentum acceleration zero-order variance reduction.By introducing a new form of loss function and adopting the idea of margin distribution,a robust SVM optimization model is established to improve the anti-noise ability of SVM.By using the zero-order variance reduction algorithm and momentum acceleration technique,a new optimization model solution method is proposed.Experimental results show that this method reduces the influence of variance effectively by introducing the gradient correction item,and increases the convergence speed of the algorithm significantly by using the momentum acceleration technology.