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

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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

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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