Robust Structured Subspace Learning for Data Representation
文献类型：期刊论文
| 作者 | Li, Zechao1; Liu, Jing2 ; Tang, Jinhui1; Lu, Hanqing2
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2015-10-01 |
| 卷号 | 37期号:10页码:2085-2098 |
| 关键词 | Data representation latent subspace image understanding feature learning structure preserving |
| 英文摘要 | 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. |
| 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 ; IMAGE ANNOTATION ; FEATURE-SELECTION ; MATRIX FACTORIZATION ; GEOMETRIC FRAMEWORK ; WEB ; CLASSIFICATION |
| 收录类别 | SCI |
| 原文出处 | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7031960 |
| 语种 | 英语 |
| WOS记录号 | WOS:000360813400011 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8968]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Nanjing Univ Sci & Technol, Sch Engn & Comp Sci, Nanjing 210094, Jiangsu, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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