CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Robust Structured Subspace Learning for Data Representation
Li, Zechao1; Liu, Jing1; Tang, Jinhui2; Lu, Hanqing1
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2015-10-01
Volume37Issue:10Pages:2085-2098
SubtypeArticle
AbstractTo uncover an appropriate latent subspace for data representation, in this paper we propose a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image understanding and feature learning into a joint learning framework. The learned subspace is adopted as an intermediate space to reduce the semantic gap between the low-level visual features and the high-level semantics. To guarantee the subspace to be compact and discriminative, the intrinsic geometric structure of data, and the local and global structural consistencies over labels are exploited simultaneously in the proposed algorithm. Besides, we adopt the l(2,1)-norm for the formulations of loss function and regularization respectively to make our algorithm robust to the outliers and noise. An efficient algorithm is designed to solve the proposed optimization problem. It is noted that the proposed framework is a general one which can leverage several well-known algorithms as special cases and elucidate their intrinsic relationships. To validate the effectiveness of the proposed method, extensive experiments are conducted on diversity datasets for different image understanding tasks, i.e., image tagging, clustering, and classification, and the more encouraging results are achieved compared with some state-of-the-art approaches.
KeywordData Representation Latent Subspace Image Understanding Feature Learning Structure Preserving
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2015.2400461
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; IMAGE ANNOTATION ; FEATURE-SELECTION ; MATRIX FACTORIZATION ; GEOMETRIC FRAMEWORK ; WEB ; CLASSIFICATION
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Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000360813400011
Citation statistics
Cited Times:169[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8968
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Engn & Comp Sci, Nanjing 210094, Jiangsu, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Zechao,Liu, Jing,Tang, Jinhui,et al. Robust Structured Subspace Learning for Data Representation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2015,37(10):2085-2098.
APA Li, Zechao,Liu, Jing,Tang, Jinhui,&Lu, Hanqing.(2015).Robust Structured Subspace Learning for Data Representation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,37(10),2085-2098.
MLA Li, Zechao,et al."Robust Structured Subspace Learning for Data Representation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 37.10(2015):2085-2098.
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