南方科技大学知识苑(SUSTech KC): Unsupervised Feature Selection by Pareto Optimization

题名	Unsupervised Feature Selection by Pareto Optimization
作者	Feng, Chao 1; Qian, Chao 1; Tang, Ke2
通讯作者	Feng, Chao
发表日期	2019
会议录名称	THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
页码	3534-3541
出版地	2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
出版者	ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
摘要	Dimensionality reduction is often employed to deal with the data with a huge number of features, which can be generally divided into two categories: feature transformation and feature selection. Due to the interpretability, the efficiency during inference and the abundance of unlabeled data, unsupervised feature selection has attracted much attention. In this paper, we consider its natural formulation, column subset selection (CSS), which is to minimize the reconstruction error of a data matrix by selecting a subset of features. We propose an anytime randomized iterative approach POCSS, which minimizes the reconstruction error and the number of selected features simultaneously. Its approximation guarantee is well bounded. Empirical results exhibit the superior performance of POCSS over the state-of-the-art algorithms.
学校署名	其他
语种	英语
相关链接	[来源记录]
收录类别	CPCI-S ; EI
资助项目	NSFC[61603367] ; NSFC[61672478] ; NSFC[2016QNRC001]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000485292603068
EI入藏号	20203509102159
EI主题词	Iterative methods ; Metadata ; Multiobjective optimization ; Pareto principle
EI分类号	Optimization Techniques:921.5 ; Numerical Methods:921.6
来源库	Web of Science
引用统计	被引频次[WOS]：18
成果类型	会议论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/24517
专题	工学院_计算机科学与工程系
作者单位	1.Univ Sci & Technol China, Sch Comp Sci & Technol, Anhui Prov Key Lab Big Data Anal & Applicat, Hefei 230027, Anhui, Peoples R China 2.Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Feng, Chao,Qian, Chao,Tang, Ke. Unsupervised Feature Selection by Pareto Optimization[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2019:3534-3541.