西安电子科技大学学报（自然科学版）2019，Vol.46Issue(1)：64-72,9.DOI:10.19665/j.issn1001-2400.2019.01.011
鲁棒支持向量机及其稀疏算法
Robust support vector machines and their sparse algorithms
摘要
Abstract
Based on nonconvex and smooth loss,the robust support vector machine(RSVM)is insenstive to outliers for classification problems.However,the existing algorithms for RSVM are not suitable for dealing with large-scale problems,because they need to iteratively solve quadratic programmings,which leads to a large amount of calculation and slow convergence.To overcome this drawback,the method with a faster convergence rate is used to solve the RSVM.Then,by using the idea of least square,ageneralized exponentially robust LSSVM (ER-LSSVM)model is proposed,which is solved by the algorithm with a faster convergence rate.Moreover,the robustness of the ER-LSSVM is interpreted theoretically.Finally, ultilizing low-rank approximation of the kernel matrix,the sparse RSVM algorithm (SR-SVM)and sparse ER-LSSVM algorithm (SER-LSSVM)are proposed for handing large-scale problems.Many experimental results illustrate that the proposed algorithm outperforms the related algorithms in terms of convergence speed,test accuracy and training time.关键词
鲁棒支持向量机/非凸光滑损失/稀疏解/低秩近似Key words
robust support vector machines/nonconvex and smooth loss/sparse solution/low-rank approximation分类
信息技术与安全科学引用本文复制引用
安亚利,周水生,陈丽,王保军..鲁棒支持向量机及其稀疏算法[J].西安电子科技大学学报（自然科学版）,2019,46(1):64-72,9.基金项目
国家自然科学基金(61772020) （61772020）
