Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction

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...

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Vydáno v:	IEEE transactions on image processing Ročník 26; číslo 10; s. 5019 - 5030
Hlavní autoři:	Wang, Rong, Nie, Feiping, Hong, Richang, Chang, Xiaojun, Yang, Xiaojun, Yu, Weizhong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.10.2017
Témata:	Algorithm design and analysis Dimensionality reduction (DR) Eigenvalues and eigenfunctions Face recognition hyperspectral image (HSI) classification Laplace equations locality preserving projections (LPP) Manifolds Optimization Training data
ISSN:	1057-7149, 1941-0042, 1941-0042
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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.
AbstractList	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.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. 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.
Author	Feiping Nie Xiaojun Yang Richang Hong Xiaojun Chang Rong Wang Weizhong Yu
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Cites_doi	10.1162/089976603321780317 10.1109/TIP.2013.2253480 10.1109/TNN.2009.2015760 10.1117/1.3067869 10.1109/CVPR.1991.139758 10.1109/TMM.2016.2626959 10.1016/j.neucom.2015.06.011 10.1109/TMM.2014.2298841 10.1109/JSTARS.2014.2328601 10.1109/TIP.2006.881945 10.1109/TPAMI.2007.1131 10.1109/TMM.2015.2508146 10.1109/TIP.2013.2249077 10.1109/TPAMI.2007.250598 10.1109/TIP.2016.2593344 10.1109/34.9121 10.1145/2910585 10.1109/TNNLS.2015.2441735 10.1109/TNN.2011.2162000 10.1016/j.isprsjprs.2015.04.015 10.1109/TIP.2013.2297020 10.1109/LGRS.2013.2255097 10.1109/34.598228 10.1109/TIP.2014.2348868 10.1109/TCYB.2016.2519448 10.1016/j.patcog.2009.04.001 10.1126/science.290.5500.2319 10.1109/TNNLS.2011.2178037 10.1109/TPAMI.2005.55 10.1109/TIP.2012.2202678 10.1126/science.290.5500.2323 10.1109/TCYB.2014.2341575 10.1109/ICCV.2013.323
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References	ref34 ref12 ref37 ref15 ref36 ref14 ref30 ref11 ref32 ref10 ref2 ref1 ref39 ref38 ref18 he (ref24) 2003; 16 belkin (ref17) 2001; 14 cai (ref26) 2007 he (ref25) 2005 nie (ref33) 2011; 22 yan (ref19) 2005 ref23 ref20 jolliffe (ref13) 2002 ref22 ref21 roweis (ref16) 2000; 290 jia (ref31) 2009; 20 ref28 he (ref35) 2005; 27 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
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SubjectTerms	Algorithm design and analysis Dimensionality reduction (DR) Eigenvalues and eigenfunctions Face recognition hyperspectral image (HSI) classification Laplace equations locality preserving projections (LPP) Manifolds Optimization Training data
Title	Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction
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