| Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection | |
Hou, Chenping1; Nie, Feiping2; Li, Xuelong3 ; Yi, Dongyun1; Wu, Yi1
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| 作者部门 | 光学影像学习与分析中心 |
| 2014-06-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 卷号 | 44期号:6页码:793-804 |
| 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. |
| 文章类型 | Article |
| 关键词 | Embedding Learning Feature Selection Pattern Recognition Sparse Regression |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2013.2272642 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; FEATURE-EXTRACTION ; SPACE ; MODEL |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000337960000006 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22361 |
| 专题 | 光谱成像技术研究室 |
| 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. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Joint Embedding Lear（11197KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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