SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification
文献类型：期刊论文
| 作者 | Xie, Guo-Sen4 ; Zhang, Zheng5,6; Liu, Li; Zhu, Fan; Zhang, Xu-Yao1; Shao, Ling; Li, Xuelong2,3
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2020-10-01 |
| 卷号 | 31期号:10页码:4290-4302 |
| 关键词 | Training Task analysis Learning systems Computational modeling Optimization Support vector machines Principal component analysis Feature learning feature selection least squares subspace learning |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2019.2953675 |
| 通讯作者 | Zhang, Zheng(darrenzz219@gmail.com) |
| 英文摘要 | Feature representation learning, an emerging topic in recent years, has achieved great progress. Powerful learned features can lead to excellent classification accuracy. In this article, a selective and robust feature representation framework with a supervised constraint (SRSC) is presented. SRSC seeks a selective, robust, and discriminative subspace by transforming the original feature space into the category space. Particularly, we add a selective constraint to the transformation matrix (or classifier parameter) that can select discriminative dimensions of the input samples. Moreover, a supervised regularization is tailored to further enhance the discriminability of the subspace. To relax the hard zero-one label matrix in the category space, an additional error term is also incorporated into the framework, which can lead to a more robust transformation matrix. SRSC is formulated as a constrained least square learning (feature transforming) problem. For the SRSC problem, an inexact augmented Lagrange multiplier method (ALM) is utilized to solve it. Extensive experiments on several benchmark data sets adequately demonstrate the effectiveness and superiority of the proposed method. The proposed SRSC approach has achieved better performances than the compared counterpart methods. |
| WOS关键词 | LEAST-SQUARES REGRESSION ; FACE RECOGNITION ; DICTIONARY ; ILLUMINATION ; EIGENFACES |
| 资助项目 | National Natural Science Foundation of China[61702163] ; National Natural Science Foundation of China[61871470] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000576436600042 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42081]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Zhang, Zheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Northwestern Polytech Univ, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710072, Peoples R China 3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 4.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates 5.Harbin Inst Technol, Biocomp Res Ctr, Shenzhen 518055, Peoples R China 6.Peng Cheng Lab, Shenzhen 518055, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Guo-Sen,Zhang, Zheng,Liu, Li,et al. SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(10):4290-4302. |
| APA | Xie, Guo-Sen.,Zhang, Zheng.,Liu, Li.,Zhu, Fan.,Zhang, Xu-Yao.,...&Li, Xuelong.(2020).SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(10),4290-4302. |
| MLA | Xie, Guo-Sen,et al."SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.10(2020):4290-4302. |
入库方式： OAI收割
来源：自动化研究所
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