Inductive Robust Principal Component Analysis

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Inductive Robust Principal Component Analysis
	Bao, Bing-Kun1,2 ; Liu, Guangcan 3; Xu, Changsheng1,2 ; Yan, Shuicheng 4
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2012-08-01
卷号	21 期号:8 页码:3794-3800
关键词	Error correction robust principal component analysis (PCA) subspace learning
英文摘要	In this paper, we address the error correction problem, that is, to uncover the low-dimensional subspace structure from high-dimensional observations, which are possibly corrupted by errors. When the errors are of Gaussian distribution, principal component analysis (PCA) can find the optimal (in terms of least-square error) low-rank approximation to high-dimensional data. However, the canonical PCA method is known to be extremely fragile to the presence of gross corruptions. Recently, Wright et al. established a so-called robust principal component analysis (RPCA) method, which can well handle the grossly corrupted data. However, RPCA is a transductive method and does not handle well the new samples, which are not involved in the training procedure. Given a new datum, RPCA essentially needs to recalculate over all the data, resulting in high computational cost. So, RPCA is inappropriate for the applications that require fast online computation. To overcome this limitation, in this paper, we propose an inductive robust principal component analysis (IRPCA) method. Given a set of training data, unlike RPCA that targets on recovering the original data matrix, IRPCA aims at learning the underlying projection matrix, which can be used to efficiently remove the possible corruptions in any datum. The learning is done by solving a nuclear-norm regularized minimization problem, which is convex and can be solved in polynomial time. Extensive experiments on a benchmark human face dataset and two video surveillance datasets show that IRPCA cannot only be robust to gross corruptions, but also handle the new data well and in an efficient way.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	DISPERSION MATRICES ; COMPLETION ; SYSTEMS
收录类别	SCI
语种	英语
WOS记录号	WOS:000306598100037
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2841]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119613, Singapore 3.Univ Illinois, Urbana, IL 61801 USA 4.Natl Univ Singapore, Singapore 10000, Singapore
推荐引用方式 GB/T 7714	Bao, Bing-Kun,Liu, Guangcan,Xu, Changsheng,et al. Inductive Robust Principal Component Analysis[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(8):3794-3800.
APA	Bao, Bing-Kun,Liu, Guangcan,Xu, Changsheng,&Yan, Shuicheng.(2012).Inductive Robust Principal Component Analysis.IEEE TRANSACTIONS ON IMAGE PROCESSING,21(8),3794-3800.
MLA	Bao, Bing-Kun,et al."Inductive Robust Principal Component Analysis".IEEE TRANSACTIONS ON IMAGE PROCESSING 21.8(2012):3794-3800.