A New Kernel-Based Classification Algorithm

Zhou, X; Jiang, W; Tian, Y; Zhang, P; Nie, G; Shi, Y

A New Kernel-Based Classification Algorithm

Zhou, X Jiang, W Tian, Y Zhang, P

Nie, G Shi, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Ninth IEEE International Conference on Data Mining, 2009. ICDM '09., 2009, pp. 1094 - 1099
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Zhou, X	en_US
dc.contributor.author	Jiang, W	en_US
dc.contributor.author	Tian, Y	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Nie, G	en_US
dc.contributor.author	Shi, Y	en_US
dc.contributor.editor	NA	en_US
dc.date	2009-12-06	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Ninth IEEE International Conference on Data Mining, 2009. ICDM '09., 2009, pp. 1094 - 1099	en_US
dc.identifier.isbn	978-1-4244-5242-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28974
dc.description.abstract	A new kernel-based learning algorithm called kernel affine subspace nearest point (KASNP) approach is proposed in this paper. Inspired by the geometrical explanation of support vector machines (SVMs) and its nearest point problem in convex hulls, we extend the convex hull of each class to its corresponding affine subspace in high dimensional space induced by kernel. In two class affine subspaces, KASNP finds the nearest points and then constructs a separating hyperplane, which bisects the line segment joining them. The nearest point problem of KASNP is only an unconstrained optimal problem whose solution can be directly computed. Compared with SVM, KASNP avoids solving convex quadratic programming. Experiments on two-spiral dataset, two UCI credit datasets, and face recognition datasets show that our proposed KASNP is effective for data classification.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Ninth IEEE International Conference on Data Mining, 2009. ICDM '09.	en_US
dc.relation.ispartof	International Conference on Data Mining	en_US
dc.relation.isbasedon	10.1109/ICDM.2009.80	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A New Kernel-Based Classification Algorithm	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Miami, USA	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Miami, USA	en_US
pubs.embargo.period	Not known	en_US
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
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2009-12-06	en_US

Abstract:

A new kernel-based learning algorithm called kernel affine subspace nearest point (KASNP) approach is proposed in this paper. Inspired by the geometrical explanation of support vector machines (SVMs) and its nearest point problem in convex hulls, we extend the convex hull of each class to its corresponding affine subspace in high dimensional space induced by kernel. In two class affine subspaces, KASNP finds the nearest points and then constructs a separating hyperplane, which bisects the line segment joining them. The nearest point problem of KASNP is only an unconstrained optimal problem whose solution can be directly computed. Compared with SVM, KASNP avoids solving convex quadratic programming. Experiments on two-spiral dataset, two UCI credit datasets, and face recognition datasets show that our proposed KASNP is effective for data classification.

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http://hdl.handle.net/10453/28974