Sparse Least Squares Low Rank Kernel Machines

被引：0

作者：

Xu, Di ^{[1
]}

Fang, Manjing ^{[1
]}

Hong, Xia ^{[2
]}

Gao, Junbin ^{[1
]}

机构：

[1] Univ Sydney, Univ Sydney Business Sch, Discipline Business Analyt, Sydney, NSW 2006, Australia

[2] Univ Reading, Dept Comp Sci, Reading RG6 6AH, Berks, England

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2019), PT II | 2019年 / 11954卷

关键词：

Least squares support vector machine; Low Rank Kernels; Robust RBF function; End-to-end modeling;

D O I：

10.1007/978-3-030-36711-4_33

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A general framework of low rank least squares support vector machine (LR-LSSVM) is introduced in this paper. The special structure of controlled model size of the low rank kernel machine brings in remarkable sparsity and hence gigantic breakthrough in computational efficiency. In the meantime, a two-step optimization algorithm with three regimes for gradient descent is proposed. For demonstration purpose, experiments are carried out using a novel robust radial basis function (RRBF), the performances of which mostly dominate.

引用

页码：395 / 406

页数：12

共 13 条

[1] Improving support vector machine classifiers by modifying kernel functions [J].

Amari, S ;

Wu, S .

NEURAL NETWORKS, 1999, 12 (06) :783-789

[2] Local regularization assisted orthogonal least squares regression [J].

Chen, S .

NEUROCOMPUTING, 2006, 69 (4-6) :559-585

[3] ORTHOGONAL LEAST-SQUARES LEARNING ALGORITHM FOR RADIAL BASIS FUNCTION NETWORKS [J].

CHEN, S ;

COWAN, CFN ;

GRANT, PM .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1991, 2 (02) :302-309

[4] Significant vector learning to construct sparse kernel regression models [J].

Gao, Junbin ;

Shi, Daming ;

Liu, Xiaomao .

NEURAL NETWORKS, 2007, 20 (07) :791-798

[5]

Hong X, 2018, IEEE T CYBERN, VXX

[6] Ramp loss least squares support vector machine [J].

Liu, Dalian ;

Shi, Yong ;

Tian, Yingjie ;

Huang, Xiankai .

JOURNAL OF COMPUTATIONAL SCIENCE, 2016, 14 :61-68

[7] A review of classification algorithms for EEG-based brain-computer interfaces [J].

Lotte, F. ;

Congedo, M. ;

Lecuyer, A. ;

Lamarche, F. ;

Arnaldi, B. .

JOURNAL OF NEURAL ENGINEERING, 2007, 4 (02) :R1-R13

[8]

Scholkopf B., 2002, LEARNING KERNELS

[9] Least squares support vector machine classifiers [J].

Suykens, JAK ;

Vandewalle, J .

NEURAL PROCESSING LETTERS, 1999, 9 (03) :293-300

[10]

Williams CKI, 2001, ADV NEUR IN, V13, P682

← 1 2 →