Joint Sparse Locality-Aware Regression for Robust Discriminative Learning

doi:10.1109/TCYB.2021.3080128

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Joint Sparse Locality-Aware Regression for Robust Discriminative Learning
	Hu, Liangchen 1; Zhang, Wensheng2,3 ; Dai, Zhenlei 1
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2021-06-23
页码	14
通讯作者	Zhang, Wensheng(zhangwenshengia@hotmail.com)
摘要	With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of weakly discriminating marginal representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant methods, we propose a more powerful discriminant feature extraction framework, namely, joint sparse locality-aware regression (JSLAR). In our model, we formulate a new strategy induced by the nonsquared L-2 norm for enhancing the local intraclass compactness of the data manifold, which can achieve the joint learning of the locality-aware graph structure and the desirable projection matrix. Besides, we formulate a weighted retargeted regression to perform the marginal representation learning adaptively instead of using the general average interclass margin. To alleviate the disturbance of outliers and prevent overfitting, we measure the regression term and locality-aware term together with the regularization term by forcing the row sparsity with the joint L-2,L-1 norms. Then, we derive an effective iterative algorithm for solving the proposed model. The experimental results over a range of benchmark databases demonstrate that the proposed JSLAR outperforms some state-of-the-art approaches.
关键词	Feature selection and extraction joint L-2,L-1-norms sparsity locality-aware graph learning marginal representation learning
DOI	10.1109/TCYB.2021.3080128
关键词[WOS]	LEAST-SQUARES REGRESSION ; RECOGNITION ; CLASSIFICATION ; SELECTION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102100] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61806202] ; National Natural Science Foundation of China[61976213]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000733526600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	机器学习
引用统计	被引频次：4[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/173211/47119
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Zhang, Wensheng
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
通讯作者单位	精密感知与控制研究中心
推荐引用方式 GB/T 7714	Hu, Liangchen,Zhang, Wensheng,Dai, Zhenlei. Joint Sparse Locality-Aware Regression for Robust Discriminative Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:14.
APA	Hu, Liangchen,Zhang, Wensheng,&Dai, Zhenlei.(2021).Joint Sparse Locality-Aware Regression for Robust Discriminative Learning.IEEE TRANSACTIONS ON CYBERNETICS,14.
MLA	Hu, Liangchen,et al."Joint Sparse Locality-Aware Regression for Robust Discriminative Learning".IEEE TRANSACTIONS ON CYBERNETICS (2021):14.