Dimensionality reduction by soft-margin support vector machine

Dong, R; Meng, H; Long, Z; Zhao, H

Dimensionality reduction by soft-margin support vector machine

Dong, R Meng, H Long, Z

Zhao, H

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Publication Type:: Conference Proceeding
Citation:: ICA 2017 - 2017 IEEE International Conference on Agents, 2017, pp. 154 - 156
Issue Date:: 2017-08-23

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Field	Value	Language
dc.contributor.author	Dong, R	en_US
dc.contributor.author	Meng, H	en_US
dc.contributor.author	Long, Z https://orcid.org/0000-0002-2714-3453	en_US
dc.contributor.author	Zhao, H	en_US
dc.date.issued	2017-08-23	en_US
dc.identifier.citation	ICA 2017 - 2017 IEEE International Conference on Agents, 2017, pp. 154 - 156	en_US
dc.identifier.isbn	9781538607688	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125966
dc.description.abstract	© 2017 IEEE. Dimensionality reduction is one of the key issues of machine learning and data mining, especially for high-dimensional data set. In the literature, there are various dimensionality reduction methods, such as PCA, LDA, and KLDA, and the difference between them mainly lies in the optimization objective. In this paper, we propose a new dimensionality reduction method, whose optimization objective is to maximize the margin between different classes, after projecting the original features into some specific lower-dimensional subspace. The specific subspace is constructed with the help of soft margin support vector machines. Our experiments based on several real-world datasets show that this method improves the performance on classification, and it not only can reduce redundant information in features but also is robust to noise.	en_US
dc.relation.ispartof	ICA 2017 - 2017 IEEE International Conference on Agents	en_US
dc.relation.isbasedon	10.1109/AGENTS.2017.8015324	en_US
dc.title	Dimensionality reduction by soft-margin support vector machine	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Dimensionality reduction is one of the key issues of machine learning and data mining, especially for high-dimensional data set. In the literature, there are various dimensionality reduction methods, such as PCA, LDA, and KLDA, and the difference between them mainly lies in the optimization objective. In this paper, we propose a new dimensionality reduction method, whose optimization objective is to maximize the margin between different classes, after projecting the original features into some specific lower-dimensional subspace. The specific subspace is constructed with the help of soft margin support vector machines. Our experiments based on several real-world datasets show that this method improves the performance on classification, and it not only can reduce redundant information in features but also is robust to noise.

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http://hdl.handle.net/10453/125966