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

Previous Articles     Next Articles

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

[1] SMITH R S,KITTLER J,HAMOUZ M,et al.Face RecognitionUsing Angular LDA and SVM Ensembles[C]∥18th International Conference on Pattern Recognition(ICPR 2006).2006:1008-1012.
[2] C J,LIN C W,HSU,et al.A practical guide to support vector classification.http://www.csie.ntu.edu.tw/ cjlin/papers/guide/guide.pdf.
[3] IVOR W T,JAMES T K,CHEUNG P K.Fast SVM Training on Very Large Data Sets[J].Journal of Machine Learning Research,2005(6):363-392.
[4] FRANC V,SONNENBURG S.Optimized cutting plane algo-rithm for large-scale risk minimization[J].Journal of Machine Learning Research,2009(10):2157-2192.
[5] MANGASARIAN O,WILD E.Multisurface proximal supportvector machine classification via generalized eigenvalues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(1),69-74.
[6] JAYADEVA,KHEMCHANDANI R,CHANDRA S.Fuzzy mul-ti-category proximal support vector classification via generalized eigenvalues[J].Soft Comput,2007,1:679-685.
[7] GUARRACINO M R,CIFARELLI C,SEREF O,et al.A Parallel Classification Method for Genomic and Proteomic Problems[C]∥20th International Conference on Advanced Information Networking and Applications(AINA’06).2006:588-592.
[8] YANG X B,CHEN S C.Proximal Support Vector MachineBased on Prototypal Multiclassfication Hyperplanes[J].Journal of Computer Research and Development,2006,3(10):1700-1705.(in Chinese) 杨绪兵,陈松灿.基于原型超平面的多类最接近支持向量机[J].计算机研究与发展,2006,43(10):1700-1705.
[9] YANG X B,CHEN S C,YANG Y M.Localized proximal support vector machine via generalized eigenvalues[J].Chinese Journal of Computers,2007,0(8):1227-1234.(in Chinese) 杨绪兵,陈松灿,杨益民.局部化的广义特征值最接近支持向量机[J].计算机学报,2007,30(8):1227-1234.
[10] YE Q L,ZHAO C X,YE N,et al.Multi-Weight Vector Projection Support vector machines[J].Pattern Recognition Letters,2010,31:2006-2011.
[11] YE Q L,YE N,YIN T M.Enhanced multi-weight vector projection support vector machine[J].Pattern Recognition Letters,2014,42:91-100.
[12] JAYADEVA,KHEMCHANDAI R,CHANDRA S.Twin sup-port vector machines for pattern classification[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2007,29(5):905-910.
[13] TIAN Y,QI Z,JU X,et al.Nonparallel support vector machines for pattern classification[J].IEEE Transactions on Cybernetics,2014,44(7):1067.
[14] CEVIKALP H.Best Fitting Hyperplanes for Classification[J].IEEE Transactions on Pattern Analysis & Machine, 2017,39(6):1076-1088.
[15] QI Z,TIAN Y,SHI Y.Structural twin support vector machine for classification[J].Knowledge-Based Syst.,2013,43:74-81.
[16] SHAO Y H,CHEN W J,WANG Z,et al.Weighted linear loss twin support vector machine for large-scale classification[J].Knowledge-Based Systems,2014,73(1):276-288.
[17] KHEMCHANDANI R,SAIGAL P,CHANDRA S.Improve-ments on ν-Twin Support Vector Machine[J].Neural Netw.,2016,7(79):97-107.
[18] KUMAR M A,GOPA M.Application of smoothing techniqueon twin support vector machines[J].Pattern Recognition Letters,2008,29:1842-1848.
[19] YE Q L,ZHAO C X.A Feature Selection Method for TWSVM via a Regularization Technique[J].Journal of Computer Research and Development,2011,8(6):1029-1037.(in Chinese) 业巧林,赵春霞.基于正则化的TWSVM 特征选择算法[J].计算机研究与发展,2011,48(6):1029-1037.
[20] KUMAR M A,GOPAL M.Least squares twin support vector machines for pattern classification[J].Expert Systems with Applications,2009,36(4):7535-7543.
[21] WANG H X,LU X S,HU Z L,et al.Fisher discriminant analysis with L1-norm[J].IEEE Trans.Cybern.,2014,6(44):828-842.
[22] LI C N,SHAO Y H,DENG N Y.Robust L1-norm non-parallel proximal support vector machine[J].Optimization,2016,65(1):1-15.
[23] ZHENG W M,LIN Z C,WANG H X.L1-norm distance Kernel Discriminant Analysis via Bayes Error Bound Optimization for Robust Feature Extraction[J].IEEE Trans.Neural Netw.,2014,4(24):793-805.
[24] WANG H,TANG Q,ZHENG W M.L1-Norm-Based Common Spatial Patterns[J].IEEE Trans.Biomed.Engineering,2012,59(3):653-662.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[3] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[4] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[5] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[6] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[7] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[8] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .