Joint Sparse Locality-Aware Regression for Robust Discriminative Learning

doi:10.1109/TCYB.2021.3080128

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Joint Sparse Locality-Aware Regression for Robust Discriminative Learning
	Hu, Liangchen 2; Zhang, Wensheng 1,3; Dai, Zhenlei 2
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2021-06-23
页码	14
关键词	Feature selection and extraction joint L-2,L-1-norms sparsity locality-aware graph learning marginal representation learning
ISSN号	2168-2267
DOI	10.1109/TCYB.2021.3080128
通讯作者	Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要	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.
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]
WOS关键词	LEAST-SQUARES REGRESSION ; RECOGNITION ; CLASSIFICATION ; SELECTION
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000733526600001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47118]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Zhang, Wensheng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
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.