Joint Sparse Locality-Aware Regression for Robust Discriminative Learning
文献类型：期刊论文
| 作者 | Hu, Liangchen2; Zhang, Wensheng1,3 ; Dai, Zhenlei2
|
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 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. |
| WOS关键词 | LEAST-SQUARES REGRESSION ; RECOGNITION ; CLASSIFICATION ; SELECTION |
| 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研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000733526600001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。