Fast distant support vector data description

Ling, Ping, You, Xiangyang, Gao, Dajin, Gao, Tao and Li, Xue (2016) Fast distant support vector data description. Memetic Computing, 1-12. doi:10.1007/s12293-016-0189-y

Author Ling, Ping
You, Xiangyang
Gao, Dajin
Gao, Tao
Li, Xue
Title Fast distant support vector data description
Journal name Memetic Computing   Check publisher's open access policy
ISSN 1865-9292
Publication date 2016-05-12
Year available 2016
Sub-type Article (original research)
DOI 10.1007/s12293-016-0189-y
Open Access Status Not yet assessed
Start page 1
End page 12
Total pages 12
Place of publication Heidelberg, Germany
Publisher Springer
Collection year 2017
Language eng
Abstract As an indispensable approach of one class classification, support vector data description (SVDD) has been studied within diverse research areas and application domains. Distant SVDD (dSVDD) is a variant of SVDD that shows higher identification accuracy. However, dSVDD is caught by the pricy cost and troublesome parameterization, which diminishes its popularity. This paper proposes a fast distant SVDD (fdSVDD) algorithm that addresses above two problems while maintaining the performance. To this end, a new objective that is equivalent to dSVDD’s original objective is proposed firstly; then the least square version of such a new objective serves as the objective of fdSVDD; finally, fdSVDD is implemented by solving a set of linear equations. To handle the parameterization problem, a data-derived heuristic is given. To foster the efficiency, fdSVDD is equipped with the reduction strategy of training data and the specification strategy of support vectors. And in the existence of negative data, fdSVDD is extended to fast parallel SVDD (fpSVDD). In experiments on real datasets, the proposed algorithms exhibit obvious improvement in efficiency and competitive behaviors compared with the peers.
Keyword Data reduction
Fast distant support vector data description
Least square version
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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