计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3069-3074.DOI: 10.11772/j.issn.1001-9081.2017.11.3069

• 第十六届中国机器学习会议(CCML 2017) • 上一篇    下一篇

基于有效迭代算法的鲁棒L1范数非平行近似支持向量机

赵彩云, 吴长勤, 葛华   

  1. 安徽科技学院 信息与网络工程学院, 安徽 蚌埠 233100
  • 收稿日期:2017-05-16 修回日期:2017-06-29 出版日期:2017-11-10 发布日期:2017-11-11
  • 通讯作者: 赵彩云
  • 作者简介:赵彩云(1982-),女,安徽南陵人,助教,硕士,主要研究方向:计算机信息管理系统、人工智能;吴长勤(1962-),男,安徽合肥人,副教授,主要研究方向:计算机信息管理系统、人工智能;葛华(1976-),男,江苏沐阳人,讲师,硕士,主要研究方向:计算机信息管理系统、人工智能。

Robust L1-norm non-parallel proximal support vector machine via efficient iterative algorithm

ZHAO Caiyun, WU Changqing, GE Hua   

  1. College of Information and Network Engineering, Anhui Science and Technology University, Bengbu Anhui 233100, China
  • Received:2017-05-16 Revised:2017-06-29 Online:2017-11-10 Published:2017-11-11

摘要: 针对鲁棒L1范数非平行近似支持向量机(L1-NPSVM)求解算法无法保证获取可靠解的问题,提出一个新颖的迭代算法来解L1-NPSVM的目标问题。首先,根据L1-NPSVM原目标问题对解具有规模不变性,将其转换为一个等价的带等式约束的最大化问题。该迭代算法在每次迭代中利用更新权机制获取每次迭代的更新解;每次迭代中,问题归结为解两个快速的线性方程问题。从理论上证明了算法的收敛性。在公共UCI数据集上,实验显示,所提算法不仅在分类性能上要远远好于L1-NPSVM,且具有相当的计算优势。

关键词: L1-范数距离, L1范数非平行近似支持向量机, 梯度上升, 线性方程, 分类

Abstract: Considering that robust L1-norm Non-parallel Proximal Support Vector Machine (L1-NPSVM) can not guarantee a reliable solution, a new iterative algorithm was proposed to solve the objective of L1-NPSVM. Since the objective problem of L1-NPSVM is invariant to the scale of solution, such that it can be transformed into a maximization problem with an equality constraint. And then the proposed iterative algorithm was used to solve it. The iterative algorithm in each iteration obtained updated solution of each iteration by using weight updating mechanism, and the problem was reduced to solve two fast linear equations in each iteration. The convergence of the algorithm was proved theoretically. Experiments on the common UCI datasets show that the proposed algorithm is not only superior to L1-NPSVM in classification performance, but also has considerable computational advantage.

Key words: L1-norm distance, L1-norm Non-parallel Proximal Support Vector Machine (L1-NPSVM), gradient ascending, linear equation, classification

中图分类号: