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Joint Sparse Locality-Aware Regression for Robust Discriminative Learning
Hu, Liangchen1; Zhang, Wensheng2,3; Dai, Zhenlei1
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2021-06-23
Pages14
Corresponding AuthorZhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractWith 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.
KeywordFeature selection and extraction joint L-2,L-1-norms sparsity locality-aware graph learning marginal representation learning
DOI10.1109/TCYB.2021.3080128
WOS KeywordLEAST-SQUARES REGRESSION ; RECOGNITION ; CLASSIFICATION ; SELECTION
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000733526600001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification机器学习
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47119
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorZhang, Wensheng
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
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.
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