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General Subspace Learning With Corrupted Training Data Via Graph Embedding

文献类型：期刊论文

作者Bao, Bing-Kun1; Liu, Guangcan2; Hong, Richang3; Yan, Shuicheng4; Xu, Changsheng1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2013-11-01
卷号22期号:11页码:4380-4393
关键词Subspace learning corrupted training data discriminant analysis graph embedding
英文摘要We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fits the undesired properties of the data, and an error container that models the gross corruptions possibly existing in the data. Given a set of data points, these three components can be learned by solving a nuclear norm regularized optimization problem, which is convex and can be efficiently solved in polynomial time. Using the method as a tool, we propose a new discriminant analysis (i.e., supervised subspace learning) algorithm called Corruptions Tolerant Discriminant Analysis (CTDA), in which the intrinsic subspace is used to capture the features with high within-class similarity, the penalty subspace takes the role of modeling the undesired features with high between-class similarity, and the error container takes charge of fitting the possible corruptions in the data. We show that CTDA can well handle the gross corruptions possibly existing in the training data, whereas previous linear discriminant analysis algorithms arguably fail in such a setting. Extensive experiments conducted on two benchmark human face data sets and one object recognition data set show that CTDA outperforms the related algorithms.
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]NONLINEAR DIMENSIONALITY REDUCTION ; ROBUST ; FRAMEWORK ; PURSUIT ; SYSTEMS
收录类别SCI
语种英语
WOS记录号WOS:000324597800018
源URL[http://ir.ia.ac.cn/handle/173211/2840]  
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, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Illinois, Champaign, IL 61820 USA
3.Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China
4.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 10000, Singapore
推荐引用方式
GB/T 7714		 Bao, Bing-Kun,Liu, Guangcan,Hong, Richang,et al. General Subspace Learning With Corrupted Training Data Via Graph Embedding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(11):4380-4393.
APA Bao, Bing-Kun,Liu, Guangcan,Hong, Richang,Yan, Shuicheng,&Xu, Changsheng.(2013).General Subspace Learning With Corrupted Training Data Via Graph Embedding.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(11),4380-4393.
MLA Bao, Bing-Kun,et al."General Subspace Learning With Corrupted Training Data Via Graph Embedding".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.11(2013):4380-4393.

入库方式： OAI收割

来源：自动化研究所

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