Robust Structured Subspace Learning for Data Representation

doi:10.1109/TPAMI.2015.2400461

	Robust Structured Subspace Learning for Data Representation
	Li, Zechao 1; Liu, Jing1 ; Tang, Jinhui 2; Lu, Hanqing1
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2015-10-01
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
DOI	10.1109/TPAMI.2015.2400461
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; IMAGE ANNOTATION ; FEATURE-SELECTION ; MATRIX FACTORIZATION ; GEOMETRIC FRAMEWORK ; WEB ; CLASSIFICATION
URL	查看原文
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：297[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	紫东太初大模型研究中心_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
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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