Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction.

Rong Wang,; Feiping Nie,; Richang Hong,; Xiaojun Chang,; Xiaojun Yang,; Weizhong Yu,

Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction.

Rong Wang, Feiping Nie, Richang Hong, Xiaojun Chang, Xiaojun Yang, Weizhong Yu,

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26, (10), pp. 5019-5030
Issue Date:: 2017-10

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Field	Value	Language
dc.contributor.author	Rong Wang,
dc.contributor.author	Feiping Nie,
dc.contributor.author	Richang Hong,
dc.contributor.author	Xiaojun Chang,
dc.contributor.author	Xiaojun Yang,
dc.contributor.author	Weizhong Yu,
dc.date.accessioned	2022-08-11T05:39:14Z
dc.date.available	2022-08-11T05:39:14Z
dc.date.issued	2017-10
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26, (10), pp. 5019-5030
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/159938
dc.description.abstract	The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive. Therefore, in this paper, we propose a fast and orthogonal version of LPP, called FOLPP, which simultaneously minimizes the locality and maximizes the globality under the orthogonal constraint. As a result, the computation burden of the proposed algorithm can be effectively alleviated compared with the OLPP algorithm. Experimental results on two face recognition data sets and two hyperspectral data sets are presented to demonstrate the effectiveness of the proposed algorithm.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Image Processing
dc.relation.isbasedon	10.1109/TIP.2017.2726188
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction.
dc.type	Journal Article
utslib.citation.volume	26
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-11T05:39:12Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	10

Abstract:

The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive. Therefore, in this paper, we propose a fast and orthogonal version of LPP, called FOLPP, which simultaneously minimizes the locality and maximizes the globality under the orthogonal constraint. As a result, the computation burden of the proposed algorithm can be effectively alleviated compared with the OLPP algorithm. Experimental results on two face recognition data sets and two hyperspectral data sets are presented to demonstrate the effectiveness of the proposed algorithm.

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http://hdl.handle.net/10453/159938