热门
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
文献类型：期刊论文
| 作者 | Hou, Chenping1; Nie, Feiping2; Li, Xuelong3 ; Yi, Dongyun1; Wu, Yi1
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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。