计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 100-105, 130.doi: 10.11896/j.issn.1002-137X.2018.04.015

• 2017年全国理论计算机科学学术年会 • 上一篇    下一篇

基于L1-范数距离的最小二乘对支持向量机

周燕萍,业巧林   

  1. 无锡科技职业学院物联网与软件技术学院 江苏 无锡214028,南京林业大学信息科学与技术学院 南京210037
  • 出版日期:2018-04-15 发布日期:2018-05-11
  • 基金资助:
    本文受江苏省自然科学基金(BK20171453)资助

L1-norm Distance Based Least Squares Twin Support Vector Machine

ZHOU Yan-ping and YE Qiao-lin   

  • Online:2018-04-15 Published:2018-05-11

摘要: 最小二乘对支持向量机(LSTSVM)是一种有效的分类技术。然而,该方法需计算点到平面的平方L2-范数距离,从而易受野值或噪声的影响。为了缓解此问题,提出了一种有效的鲁棒 LSTSVM方法,即基于L1-范数距离的LSTSVM(LSTSVML1D)。该方法由于使 用L1范数作为距离度量,因此不易受到野值或噪声数据的影响。此外,设计了一种有效的迭代算法,旨在求解目标问题,并从理论上证明了其收敛性。在人工数据集和UCI数据集上验证了LSTSVML1D 的有效性。

关键词: 最小二乘支持向量机,基于L1-范数距离的LSTSVM,L1范数距离,L2范数平方距离

Abstract: Recently,LSTSVM,as an efficient classification algorithm,was proposed.However,this algorithm computes squared L2-norm distances from planes to points,such that it is easily affected by outliers or noisy data.In order to avoid this problem,this paper presented an efficient L1-norm distance based robust LSTSVM method,termed as LSTSVML1D.LSTSVML1D computes L1-norm distances from planes to points and is not sensitive to outliers and noise.Besides,this paper designed an efficient iterative algorithm to solve the resulted objective,and proved its convergence.Experiments on artificial dataset and UCI dataset indicate the effectiveness of the proposed LSTSVML1D.

Key words: Least squares support vector machine,L1-norm distance based LSTSVM,L1-norm distance,Squared L2-norm distance

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