Robust Structured Subspace Learning for Data Representation
Li, Zechao1; Liu, Jing1; Tang, Jinhui2; Lu, Hanqing1
2015-10-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷号37期号:10页码:2085-2098
文章类型Article
摘要To 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.
关键词Data Representation Latent Subspace Image Understanding Feature Learning Structure Preserving
WOS标题词Science & Technology ; Technology
DOI10.1109/TPAMI.2015.2400461
关键词[WOS]NONLINEAR DIMENSIONALITY REDUCTION ; IMAGE ANNOTATION ; FEATURE-SELECTION ; MATRIX FACTORIZATION ; GEOMETRIC FRAMEWORK ; WEB ; CLASSIFICATION
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收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000360813400011
引用统计
被引频次:148[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8968
专题模式识别国家重点实验室_图像与视频分析
作者单位1.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
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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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