NPrSVM: Nonparallel sparse projection support vector machine with efficient algorithm

Chen, WJ; Shao, YH; Li, CN; Wang, YQ; Liu, MZ; Wang, Z

NPrSVM: Nonparallel sparse projection support vector machine with efficient algorithm

Chen, WJ Shao, YH Li, CN Wang, YQ Liu, MZ Wang, Z

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2020, 90, pp. 106142-106142
Issue Date:: 2020-05-01

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Field	Value	Language
dc.contributor.author	Chen, WJ
dc.contributor.author	Shao, YH
dc.contributor.author	Li, CN
dc.contributor.author	Wang, YQ
dc.contributor.author	Liu, MZ
dc.contributor.author	Wang, Z
dc.date.accessioned	2021-06-16T05:39:54Z
dc.date.available	2021-06-16T05:39:54Z
dc.date.issued	2020-05-01
dc.identifier.citation	Applied Soft Computing Journal, 2020, 90, pp. 106142-106142
dc.identifier.issn	1568-4946
dc.identifier.uri	http://hdl.handle.net/10453/149551
dc.description.abstract	The recently proposed projection twin support vector machine (PTSVM) is an excellent nonparallel classifier. However, PTSVM employs the least-squares loss function to measure its within-class empirical risk, resulting in several drawbacks, such as non-sparseness for decision, sensitivity to outliers, expensive matrix inversion, and inconsistency in the linear and nonlinear models. To alleviate these issues, in this paper, we propose a novel nonparallel sparse projection support vector machine (NPrSVM). Different from the original PTSVM that squeezes the projected values of within-class instances to its own class center, NPrSVM aims to cluster them as much as possible within an insensitive tube. Specifically, our NPrSVM owns the following attractive merits: (i) Benefiting from the L1-norm symmetric Hinge loss function, NPrSVM not only enjoys sparseness for decision but also improves robustness to outliers. (ii) The elegant formulation of dual problems in NPrSVM no longer involves the matrix inversion during the training procedure. This greatly saves the computing time compared to PTSVM. (iii) While the nonlinear formulation of PTSVM is not the direct extension of linear PTSVM, the linear and nonlinear versions of our NPrSVM are consistent. (iv) An efficient dual coordinate descent algorithm is further designed for NPrSVM to handle large-scale classification. Finally, the feasibility and effectiveness of NPrSVM are validated by extensive experiments on both synthetic and real-world datasets.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Applied Soft Computing Journal
dc.relation.isbasedon	10.1016/j.asoc.2020.106142
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	NPrSVM: Nonparallel sparse projection support vector machine with efficient algorithm
dc.type	Journal Article
utslib.citation.volume	90
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-06-16T05:39:52Z
pubs.publication-status	Published
pubs.volume	90

Abstract:

The recently proposed projection twin support vector machine (PTSVM) is an excellent nonparallel classifier. However, PTSVM employs the least-squares loss function to measure its within-class empirical risk, resulting in several drawbacks, such as non-sparseness for decision, sensitivity to outliers, expensive matrix inversion, and inconsistency in the linear and nonlinear models. To alleviate these issues, in this paper, we propose a novel nonparallel sparse projection support vector machine (NPrSVM). Different from the original PTSVM that squeezes the projected values of within-class instances to its own class center, NPrSVM aims to cluster them as much as possible within an insensitive tube. Specifically, our NPrSVM owns the following attractive merits: (i) Benefiting from the L1-norm symmetric Hinge loss function, NPrSVM not only enjoys sparseness for decision but also improves robustness to outliers. (ii) The elegant formulation of dual problems in NPrSVM no longer involves the matrix inversion during the training procedure. This greatly saves the computing time compared to PTSVM. (iii) While the nonlinear formulation of PTSVM is not the direct extension of linear PTSVM, the linear and nonlinear versions of our NPrSVM are consistent. (iv) An efficient dual coordinate descent algorithm is further designed for NPrSVM to handle large-scale classification. Finally, the feasibility and effectiveness of NPrSVM are validated by extensive experiments on both synthetic and real-world datasets.

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http://hdl.handle.net/10453/149551