| Locality and similarity preserving embedding for feature selection | |
Fang, Xiaozhao1; Xu, Yong1,2; Li, Xuelong3 ; Fan, Zizhu1; Liu, Hong4; Chen, Yan5
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| 作者部门 | 光学影像学习与分析中心 |
| 2014-03-27 | |
| 发表期刊 | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 卷号 | 128页码:304-315 |
| 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. |
| 文章类型 | Article |
| 关键词 | Feature Selection Locality And Similarity Preserving Sparse Reconstruction Transformation Matrix Discriminating Information |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2013.08.040 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; FACE RECOGNITION ; MUTUAL INFORMATION ; PROJECTIONS ; EXTENSIONS ; EIGENMAPS ; FRAMEWORK ; KPCA |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000331851700037 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22403 |
| 专题 | 光谱成像技术研究室 |
| 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. |
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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 | ||
| Locality and similar（3153KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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