SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification

doi:10.1109/TNNLS.2019.2953675

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification
	Xie, Guo-Sen1 ; Zhang, Zheng2,3 ; Liu, Li ; Zhu, Fan ; Zhang, Xu-Yao4 ; Shao, Ling ; Li, Xuelong 5,6
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-10-01
卷号	31 期号:10 页码:4290-4302
通讯作者	Zhang, Zheng(darrenzz219@gmail.com)
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.
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
DOI	10.1109/TNNLS.2019.2953675
关键词[WOS]	LEAST-SQUARES REGRESSION ; FACE RECOGNITION ; DICTIONARY ; ILLUMINATION ; EIGENFACES
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61702163] ; National Natural Science Foundation of China[61871470]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000576436600042
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：28[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/173211/42082
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Zhang, Zheng
作者单位	1.Incept Inst Artificial Intelligence, Abu Dhabi, U Arab Emirates 2.Harbin Inst Technol, Biocomp Res Ctr, Shenzhen 518055, Peoples R China 3.Peng Cheng Lab, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 6.Northwestern Polytech Univ, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710072, 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.