Low-Rank Preserving Projections

doi:10.1109/TCYB.2015.2457611

OPT OpenIR > 光谱成像技术研究室

	Low-Rank Preserving Projections
	Lu, Yuwu 1,2; Lai, Zhihui 3,4; Xu, Yong 2; Li, Xuelong5 ; Zhang, David 6; Yuan, Chun 1
作者部门	光学影像学习与分析中心
	2016-08-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:8 页码:1900-1913
产权排序	5
摘要	As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L-21 norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.
文章类型	Article
关键词	Face Recognition Image Classification Locality Preserving Projections (Lpp) Low-rank Representation (Lrr)
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2457611
收录类别	SCI ; EI
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; FACE-RECOGNITION ; FRAMEWORK ; GRAPH ; LDA ; LAPLACIANFACES ; ALGORITHMS ; PCA
语种	英语
WOS研究方向	Computer Science
项目资助者	Natural Science Foundation of China(61203376 ; National Significant Science and Technology Projects of China(2013ZX01039001-002-003) ; Shenzhen Municipal Science and Technology Innovation Council(JCYJ20130329151843309 ; 61300032 ; JCYJ20140904154630436) ; 61375012 ; 61362031 ; 61170253 ; 61370163)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000379984500017
引用统计	被引频次：128[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/28171
专题	光谱成像技术研究室
作者单位	1.Tsinghua Univ, Grad Sch Shenzhen, Tsinghua CUHK Joint Res Ctr Media Sci Technol & S, Shenzhen 518055, Peoples R China 2.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China 3.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China 4.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China 5.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr OPT Imagery Anal & Learning, Xian 710119, Peoples R China 6.Hong Kong Polytech Univ, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Lu, Yuwu,Lai, Zhihui,Xu, Yong,et al. Low-Rank Preserving Projections[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(8):1900-1913.
APA	Lu, Yuwu,Lai, Zhihui,Xu, Yong,Li, Xuelong,Zhang, David,&Yuan, Chun.(2016).Low-Rank Preserving Projections.IEEE TRANSACTIONS ON CYBERNETICS,46(8),1900-1913.
MLA	Lu, Yuwu,et al."Low-Rank Preserving Projections".IEEE TRANSACTIONS ON CYBERNETICS 46.8(2016):1900-1913.

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