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Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression

文献类型：期刊论文

作者Wang, Jianji5,8; Zhang, Shupei1,3; Liu, Qi1,3; Du, Shaoyi5,8; Guo, Yu-Cheng3,6; Zheng, Nanning5,8; Wang, Fei-Yue2,4,7
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2021-04-21
页码10
关键词Correlation Matching pursuit algorithms Approximation algorithms Robots Multivariate regression Linear approximation Encoding Conditional uncorrelation dental age assessment multivariate correlation sparse coding sparse regression subset selection
ISSN号2168-2267
DOI10.1109/TCYB.2021.3062842
通讯作者Zheng, Nanning() ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要Given m d-dimensional responsors and n d-dimensional predictors, sparse regression finds at most k predictors for each responsor for linear approximation, 1 <= k <= d-1. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. In recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid to the nonapproximate method of subset selection, which is very necessary for many questions in data analysis. Here, we consider sparse regression from the view of correlation and propose the formula of conditional uncorrelation. Then, an efficient nonapproximate method of subset selection is proposed in which we do not need to calculate any coefficients in the regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from O([1/6]k(3)+(m+1)k(2)+mkd) to O([1/6]k(3)+[1/2](m+1)k(2)) for each candidate subset in sparse regression. Because the dimension d is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of nonapproximate subset selection. We also apply the proposed method in real scenarios of dental age assessment and sparse coding to validate the efficiency of the proposed method.
WOS关键词AGE ESTIMATION ; REPRESENTATION
资助项目National Key Research and Development Program of China[2016YFB1000903] ; National Natural Science Foundation of China (NSFC)[62088102] ; Key Project of Trico-Robot Plan of NSFC[91748208]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000732304400001
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 (NSFC) ; Key Project of Trico-Robot Plan of NSFC
源URL[http://ir.ia.ac.cn/handle/173211/46860]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Zheng, Nanning; Wang, Fei-Yue
作者单位1.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Coll Artificial Intelligence, Xian 710049, Peoples R China
4.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China
5.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
6.Xi An Jiao Tong Univ, Coll Stomatol, Key Lab Shaanxi Prov Craniofacial Precis Med Res, Xian 710004, Peoples R China
7.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
8.Xi An Jiao Tong Univ, Natl Engn Lab Visual Informat Proc & Applicat, Coll Artificial Intelligence, Xian 710049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jianji,Zhang, Shupei,Liu, Qi,et al. Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:10.
APA Wang, Jianji.,Zhang, Shupei.,Liu, Qi.,Du, Shaoyi.,Guo, Yu-Cheng.,...&Wang, Fei-Yue.(2021).Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression.IEEE TRANSACTIONS ON CYBERNETICS,10.
MLA Wang, Jianji,et al."Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression".IEEE TRANSACTIONS ON CYBERNETICS (2021):10.

入库方式： OAI收割

来源：自动化研究所

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