Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
	Hou, Chenping 1; Nie, Feiping 2; Li, Xuelong3 ; Yi, Dongyun 1; Wu, Yi 1
刊名	ieee transactions on cybernetics
	2014-06-01
卷号	44 期号:6 页码:793-804
关键词	Embedding learning feature selection pattern recognition sparse regression
ISSN号	2168-2267
英文摘要	feature selection has aroused considerable research interests during the last few decades. traditional learning-based feature selection methods separate embedding learning and feature ranking. in this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (jelsr), in which the embedding learning and sparse regression are jointly performed. specifically, the proposed jelsr joins embedding learning with sparse regression to perform feature selection. to show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the l(2,1)-norm regularization, and design an effective algorithm to solve the corresponding optimization problem. furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. in all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	nonlinear dimensionality reduction ; feature-extraction ; space ; model
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000337960000006
公开日期	2015-03-18
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/22361]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Natl Univ Def Technol, Dept Math & Syst Sci, Changsha 410073, Hunan, Peoples R China 2.Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Hou, Chenping,Nie, Feiping,Li, Xuelong,et al. Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection[J]. ieee transactions on cybernetics,2014,44(6):793-804.
APA	Hou, Chenping,Nie, Feiping,Li, Xuelong,Yi, Dongyun,&Wu, Yi.(2014).Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection.ieee transactions on cybernetics,44(6),793-804.
MLA	Hou, Chenping,et al."Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection".ieee transactions on cybernetics 44.6(2014):793-804.