Orthogonal sparse linear discriminant analysis

Liu, Z; Liu, G; Pu, J; Wang, X; Wang, H

Orthogonal sparse linear discriminant analysis

Liu, Z

Liu, G Pu, J Wang, X Wang, H

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Publication Type:: Journal Article
Citation:: International Journal of Systems Science, 2018, 49 (4), pp. 847 - 857
Issue Date:: 2018-03-12

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Field	Value	Language
dc.contributor.author	Liu, Z https://orcid.org/0000-0001-9816-7471	en_US
dc.contributor.author	Liu, G	en_US
dc.contributor.author	Pu, J	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Wang, H	en_US
dc.date.issued	2018-03-12	en_US
dc.identifier.citation	International Journal of Systems Science, 2018, 49 (4), pp. 847 - 857	en_US
dc.identifier.issn	0020-7721	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132417
dc.description.abstract	© 2018 Informa UK Limited, trading as Taylor & Francis Group. Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.	en_US
dc.relation.ispartof	International Journal of Systems Science	en_US
dc.relation.isbasedon	10.1080/00207721.2018.1424964	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Orthogonal sparse linear discriminant analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	49	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	49	en_US

Abstract:

© 2018 Informa UK Limited, trading as Taylor & Francis Group. Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

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http://hdl.handle.net/10453/132417