Sparse Least Square Support Vector Machines based on Random Entropy
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- 英文论文题名：Sparse Least Square Support Vector Machines based on Random Entropy
- 论文作者：Wenlu Ma ; Han Liu
- 英文论文作者：Wenlu Ma ; Han Liu ; Faculty of Automation and Information Engineering ; Xi'an University of Technology ; Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing ; Faculty of Automation and Information Engineering ; Xi’an University of Technology
- 年：2017
- 作者机构：Faculty of Automation and Information Engineering,Xi'an University of Technology;Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing,Faculty of Automation and Information Engineering,Xi’an University of Technology;
- 英文论文关键词：Sparsity LSSVM ; Large-scale data ; Random Entropy
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TP181
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：445k
- 原文格式：D
- 会议级别：全国
摘要
Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.
Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.
Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.
引文
[1]J.A.K.Suykens,L.Lukas,J.Vandewalle.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
[2]J.A.K.Suykens,L.Lukas,J.Vandewalle.Sparse approximation using least squares support vector machines[J].IEEE International Symposium on Circuits and Systems.Geneva,Switzerland,2000:757-760.
[3]G.S.El-tawel,A.K.Helmy.An edge detection scheme based on least squares support vector machine in a contourlet HMT domain[J].Applied Soft Computing,2015,26(1):418-427.
[4]X.Zeng,X.Chen.SMO-based pruning methods for sparse least squares support vector machines[J].IEEE Transactions on Neural Networks,2005,16(6):1541-1546.
[5]G.C.Cawley,N.L.C.Talbot.Improved sparse least-squares support vector machines[J].Neurocomputing,2002:1025-1031.
[6]M.Espinoza,J.A.K.Suykens,B.D.Moor.Fixed-size least squares support vector machines:A large scale application in electrical load forecasting[J].Computational Management Science,2006,3(2):113-129.
[7]Y.Li,C.Lin,W.Zhang.Improved sparse least-squares support vector machine classifiers[J].Neurocomputing,2006,69(13):1655–1658.
[8]K.De Brabanter,J.De Brabanter,J.A.K.Suykens,B.De Moor.Optimized fixed-size kernel models for large data sets[J].Comput Statist Data Anal,2010,54(6):1484–1504.
[9]P.Wang,A.J.Yan.Improved pruning algorithm using quadratic Renyi entropy for LS-SVM modeling[C].Proceedings of the 24th Chinese Control and Decision Conference,2012:3471-3474.
[10]R.Mall,J.A.K.Suykens.Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data[C].Advances in Knowledge Discovery and Data Mining,2013:161-173.
[11]Y.P.Zhao,J.G.Sun,Z.H.Du,et al.An improved recursive reduced least squares support vector regression[J].Neurocomputing,2012,87:1-9.
[12]Y.P.Zhao,J.G.Sun,Z.H.Du,et al.A pruning method of refining recursive reduced least squares support vector regression[J].Information Sciences,2015,296:160-174.
[13]R.Mall,J.A.K.Suykens.Very Sparse LSSVM Reductions for Large-Scale Data[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(5):1086-1097.
[14]B.Liu,H.Xiang.An Approximate Linear Solver in Least Square Support Vector Machine Using Randomized Singular Value Decomposition[J].Wuhan University Journal of Natural Sciences,2015,20(4):283-290.
[15]J.Lopez,K.De Brabanter,J.R.Dorronsoro,J.A.K.Suykens.Sparse LSSVMs with L0-norm minimization[C].European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning,2011:189 194.
[16]J.J.Zou,Z.T.Yu,H.Y.Zong,X.Zhao.Active Learning for Sparse Least Squares Support Vector Machines[C].Artificial Intelligence and Computational Intelligence,2011:672–679.
[17]G.Q.Si,J.Q.Shi,Z.Guo,W.L.Zhao.Efficient Sparse Least Squares Support Vector Machines for Regression[C].Proceedings of the 33rd Chinese Control Conference,2014:5173-5179.
[18]V.Ceperic,G.Gielen,A.Baric.Sparseε-tube support vector regression by active learning[J].Soft Computing,2014,18:1113-1126.
[19]G.Q.Si,J.Q.Shi,Z.Guo,H.Gao.An Improved Active Learning Sparse Least Squares Support Vector Machines for Regression[C].27th Chinese Control and Decision Conference,2015:4558-4562.
[20]R.P.Pablo,C.R.Juan.An algorithm for training a large-scale support vector machine for regression based on linear programming and decomposition methods[J].Pattern Recognition Letters,2013,34:439-451.
[21]S.S.Zhou.Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems[J].IEEE Transactions on Neural Networks and Learning Systems,2015:1-13.
[22]M.Chandorkar,R.Mall,J.A.K.Suykens,B.D.Moor.Fixed-Size least squares support vector machines:scala implementation for large scale classification[J].IEEE Symposium Series on Computational Intelligence,2015:522-528.
[23]S.Y.Nan,L.Sun,B.D.Chen,Z.P.Nan.Density dependent quantized least squares support vector machine for large data sets[J].IEEE Transactions on Neural Networks and Learning Systems.2016:1-13.
[24]C.L.Blake,C.J.Merz:UCI repository of machine learning databases,Irvine,CA,1998.http://archive.ics.uci.edu/ml/datasets.html.
[25]S.S.Keerthi,O.Chapelle,D.De Coste.Building support vector machines with reduced classifier complexity[J].Journal of Machine Learning Research,2006,7:1493–1515.
[26]R.Mall,J.A.K.Suykens.Sparse reductions for fixed-size least squares support vector machines on large scale data[C].17th Pacific-Asia Conf.Knowl.Discovery Data Mining,2013.
[2]J.A.K.Suykens,L.Lukas,J.Vandewalle.Sparse approximation using least squares support vector machines[J].IEEE International Symposium on Circuits and Systems.Geneva,Switzerland,2000:757-760.
[3]G.S.El-tawel,A.K.Helmy.An edge detection scheme based on least squares support vector machine in a contourlet HMT domain[J].Applied Soft Computing,2015,26(1):418-427.
[4]X.Zeng,X.Chen.SMO-based pruning methods for sparse least squares support vector machines[J].IEEE Transactions on Neural Networks,2005,16(6):1541-1546.
[5]G.C.Cawley,N.L.C.Talbot.Improved sparse least-squares support vector machines[J].Neurocomputing,2002:1025-1031.
[6]M.Espinoza,J.A.K.Suykens,B.D.Moor.Fixed-size least squares support vector machines:A large scale application in electrical load forecasting[J].Computational Management Science,2006,3(2):113-129.
[7]Y.Li,C.Lin,W.Zhang.Improved sparse least-squares support vector machine classifiers[J].Neurocomputing,2006,69(13):1655–1658.
[8]K.De Brabanter,J.De Brabanter,J.A.K.Suykens,B.De Moor.Optimized fixed-size kernel models for large data sets[J].Comput Statist Data Anal,2010,54(6):1484–1504.
[9]P.Wang,A.J.Yan.Improved pruning algorithm using quadratic Renyi entropy for LS-SVM modeling[C].Proceedings of the 24th Chinese Control and Decision Conference,2012:3471-3474.
[10]R.Mall,J.A.K.Suykens.Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data[C].Advances in Knowledge Discovery and Data Mining,2013:161-173.
[11]Y.P.Zhao,J.G.Sun,Z.H.Du,et al.An improved recursive reduced least squares support vector regression[J].Neurocomputing,2012,87:1-9.
[12]Y.P.Zhao,J.G.Sun,Z.H.Du,et al.A pruning method of refining recursive reduced least squares support vector regression[J].Information Sciences,2015,296:160-174.
[13]R.Mall,J.A.K.Suykens.Very Sparse LSSVM Reductions for Large-Scale Data[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(5):1086-1097.
[14]B.Liu,H.Xiang.An Approximate Linear Solver in Least Square Support Vector Machine Using Randomized Singular Value Decomposition[J].Wuhan University Journal of Natural Sciences,2015,20(4):283-290.
[15]J.Lopez,K.De Brabanter,J.R.Dorronsoro,J.A.K.Suykens.Sparse LSSVMs with L0-norm minimization[C].European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning,2011:189 194.
[16]J.J.Zou,Z.T.Yu,H.Y.Zong,X.Zhao.Active Learning for Sparse Least Squares Support Vector Machines[C].Artificial Intelligence and Computational Intelligence,2011:672–679.
[17]G.Q.Si,J.Q.Shi,Z.Guo,W.L.Zhao.Efficient Sparse Least Squares Support Vector Machines for Regression[C].Proceedings of the 33rd Chinese Control Conference,2014:5173-5179.
[18]V.Ceperic,G.Gielen,A.Baric.Sparseε-tube support vector regression by active learning[J].Soft Computing,2014,18:1113-1126.
[19]G.Q.Si,J.Q.Shi,Z.Guo,H.Gao.An Improved Active Learning Sparse Least Squares Support Vector Machines for Regression[C].27th Chinese Control and Decision Conference,2015:4558-4562.
[20]R.P.Pablo,C.R.Juan.An algorithm for training a large-scale support vector machine for regression based on linear programming and decomposition methods[J].Pattern Recognition Letters,2013,34:439-451.
[21]S.S.Zhou.Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems[J].IEEE Transactions on Neural Networks and Learning Systems,2015:1-13.
[22]M.Chandorkar,R.Mall,J.A.K.Suykens,B.D.Moor.Fixed-Size least squares support vector machines:scala implementation for large scale classification[J].IEEE Symposium Series on Computational Intelligence,2015:522-528.
[23]S.Y.Nan,L.Sun,B.D.Chen,Z.P.Nan.Density dependent quantized least squares support vector machine for large data sets[J].IEEE Transactions on Neural Networks and Learning Systems.2016:1-13.
[24]C.L.Blake,C.J.Merz:UCI repository of machine learning databases,Irvine,CA,1998.http://archive.ics.uci.edu/ml/datasets.html.
[25]S.S.Keerthi,O.Chapelle,D.De Coste.Building support vector machines with reduced classifier complexity[J].Journal of Machine Learning Research,2006,7:1493–1515.
[26]R.Mall,J.A.K.Suykens.Sparse reductions for fixed-size least squares support vector machines on large scale data[C].17th Pacific-Asia Conf.Knowl.Discovery Data Mining,2013.