Locality and similarity preserving embedding for feature selection
文献类型：期刊论文
| 作者 | Fang, Xiaozhao1; Xu, Yong1,2; Li, Xuelong3 ; Fan, Zizhu1; Liu, Hong4; Chen, Yan5
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| 刊名 | neurocomputing
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| 出版日期 | 2014-03-27 |
| 卷号 | 128页码:304-315 |
| 关键词 | Feature selection Locality and similarity preserving Sparse reconstruction Transformation matrix Discriminating information |
| ISSN号 | 0925-2312 |
| 英文摘要 | feature selection (fs) methods have commonly been used as a main way to select the relevant features. in this paper, we propose a novel unsupervised fs method, i.e., locality and similarity preserving embedding (lspe) for feature selection. specifically, the nearest neighbor graph is firstly constructed to preserve the locality structure of data points, and then this locality structure is mapped to the reconstruction coefficients such that the similarity among these data points is preserved. moreover, the sparsity derived by the locality is also preserved. finally, the low dimensional embedding of the sparse reconstruction is evaluated to best preserve the locality and similarity. we impose l(2.1)-norm on the transformation matrix to achieve row-sparsity, which allows us to select relevant features and learn the embedding simultaneously. the selected features have good stability due to the locality and similarity preserving, and more importantly, they contain natural discriminating information even if no class labels are provided. we present the optimization algorithm and analysis of convergence of the proposed method. the extensive experimental results show the effectiveness of the proposed method. (c) 2013 elsevier b.v. all rights reserved. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | nonlinear dimensionality reduction ; face recognition ; mutual information ; projections ; extensions ; eigenmaps ; framework ; kpca |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000331851700037 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22403]  |
| 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.Shenzhen Grad Sch, Harbin Inst Technol, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China 2.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Guangdong, Peoples R China 3.Chinese Acad Sci, XPan Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China 4.Peking Univ, Shenzhen Grad Sch, Engn Lab Intelligent Percept Internet Things, Shenzhen 518055, Guangdong, Peoples R China 5.Shenzhen Sunwin Intelligent Co Ltd, Shenzhen 518057, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Locality and similarity preserving embedding for feature selection[J]. neurocomputing,2014,128:304-315. |
| APA | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,Fan, Zizhu,Liu, Hong,&Chen, Yan.(2014).Locality and similarity preserving embedding for feature selection.neurocomputing,128,304-315. |
| MLA | Fang, Xiaozhao,et al."Locality and similarity preserving embedding for feature selection".neurocomputing 128(2014):304-315. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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