ν-projection twin support vector machine for pattern classification

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

ν-projection twin support vector machine for pattern classification

Chen, WJ Shao, YH Li, CN Liu, MZ Wang, Z Deng, NY

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Neurocomputing, 2020, 376, pp. 10-24
Issue Date:: 2020-02-01

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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	Liu, MZ
dc.contributor.author	Wang, Z
dc.contributor.author	Deng, NY
dc.date.accessioned	2021-06-20T23:45:46Z
dc.date.available	2021-06-20T23:45:46Z
dc.date.issued	2020-02-01
dc.identifier.citation	Neurocomputing, 2020, 376, pp. 10-24
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/149662
dc.description.abstract	In this paper, we improve the projection twin support vector machine (PTSVM) to a novel nonparallel classifier, termed as ν-PTSVM. Specifically, our ν-PTSVM aims to seek an optimal projection for each class such that, in each projection direction, instances of their own class are clustered around their class center while keep instances of the other class at least one distance away from such center. Different from PTSVM, our ν-PTSVM enjoys the following characteristics: (i) ν-PTSVM is equipped by a more theoretically sound parameter ν, which can be used to control the bounds of fraction of both support vectors and margin-error instances. (ii) By reformulating the least-square loss of within-class instances in primal problems of ν-PTSVM, its dual problems no longer involve the time-costly matrix inversion. (iii) ν-PTSVM behaves consistent between its linear and nonlinear cases. Namely, the kernel trick can be applied directly to ν-PTSVM for its nonlinear extension. Experimental evaluations on both synthetic and real-world datasets demonstrate the feasibility and effectiveness of the proposed approach.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2019.09.069
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	ν-projection twin support vector machine for pattern classification
dc.type	Journal Article
utslib.citation.volume	376
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
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	2021-06-20T23:45:43Z
pubs.publication-status	Published
pubs.volume	376

Abstract:

In this paper, we improve the projection twin support vector machine (PTSVM) to a novel nonparallel classifier, termed as ν-PTSVM. Specifically, our ν-PTSVM aims to seek an optimal projection for each class such that, in each projection direction, instances of their own class are clustered around their class center while keep instances of the other class at least one distance away from such center. Different from PTSVM, our ν-PTSVM enjoys the following characteristics: (i) ν-PTSVM is equipped by a more theoretically sound parameter ν, which can be used to control the bounds of fraction of both support vectors and margin-error instances. (ii) By reformulating the least-square loss of within-class instances in primal problems of ν-PTSVM, its dual problems no longer involve the time-costly matrix inversion. (iii) ν-PTSVM behaves consistent between its linear and nonlinear cases. Namely, the kernel trick can be applied directly to ν-PTSVM for its nonlinear extension. Experimental evaluations on both synthetic and real-world datasets demonstrate the feasibility and effectiveness of the proposed approach.

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http://hdl.handle.net/10453/149662