Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression

doi:10.1109/TCYB.2021.3062842

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression
	Wang, Jianji 1,2; Zhang, Shupei 3,4; Liu, Qi 3,4; Du, Shaoyi 1,2; Guo, Yu-Cheng 3,5; Zheng, Nanning 1,2; Wang, Fei-Yue6,7,8
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2021-04-21
页码	10
通讯作者	Zheng, Nanning() ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
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.
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
DOI	10.1109/TCYB.2021.3062842
关键词[WOS]	AGE ESTIMATION ; REPRESENTATION
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Key Project of Trico-Robot Plan of NSFC
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:000732304400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	人工智能基础理论
引用统计	被引频次：5[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/173211/46861
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Zheng, Nanning; Wang, Fei-Yue
作者单位	1.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China 2.Xi An Jiao Tong Univ, Natl Engn Lab Visual Informat Proc & Applicat, Coll Artificial Intelligence, Xian 710049, Peoples R China 3.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Coll Artificial Intelligence, Xian 710049, Peoples R China 4.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China 5.Xi An Jiao Tong Univ, Coll Stomatol, Key Lab Shaanxi Prov Craniofacial Precis Med Res, Xian 710004, Peoples R China 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 7.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 8.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
通讯作者单位	中国科学院自动化研究所
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.