| Hybrid structure for robust dimensionality reduction |
| Lu, Xiaoqiang ; Yuan, Yuan
|
| 作者部门 | 光学影像学习与分析中心
|
| 2014-01-26
|
| 发表期刊 | NEUROCOMPUTING
 |
| ISSN | 0925-2312
|
| 卷号 | 124期号:SI页码:131-138 |
| 摘要 | In recent years, dimensionality reduction has attracted a great deal of attention in the communities of machine learning and data mining. The basic goal of dimensionality reduction is to discover the low dimensional manifold embedded in a high dimensional space. Although some existing manifold learning algorithms (ISOMAP, LE, LLE, LTSA, etc.) can capture the local structure of data manifold, they have poor performance in some recognition tasks. This is mainly because that they cannot handle well with the "out of sample" problem. Moreover, these algorithms are sensitive to the choice of nearest neighbors, which is crucial in classification. To address these problems, this paper proposes a Robust Dimensionality Reduction Algorithm With Local and Global Structure (RLGS) based on a novel adaptive weighting mechanism. Hybrid structure of local and global structures is studied. By using the adaptive weight, RLGS has the capacity of adaptively exploiting non-linear structure of data manifold and is robust to parameters. Experiments demonstrate that RLGS performs better on public face databases compared with other reported algorithms. (C) 2013 Elsevier B.V. All rights reserved. |
| 文章类型 | Article
|
| 关键词 | Dimensionality Reduction
Pattern Recognition
Manifold Learning
Unsupervised Learning
Face Recognition
|
| WOS标题词 | Science & Technology
; Technology
|
| DOI | 10.1016/j.neucom.2013.07.019
|
| 收录类别 | SCI
; EI
|
| 关键词[WOS] | TANGENT-SPACE ALIGNMENT
; FACE-RECOGNITION
; FRAMEWORK
|
| 语种 | 英语
|
| WOS研究方向 | Computer Science
|
| WOS类目 | Computer Science, Artificial Intelligence
|
| WOS记录号 | WOS:000326853600016
|
| 引用统计 |
|
| 文献类型 | 期刊论文
|
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22395
|
| 专题 | 光谱成像技术研究室
|
| 作者单位 | Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
|
推荐引用方式
GB/T 7714 |
Lu, Xiaoqiang,Yuan, Yuan. Hybrid structure for robust dimensionality reduction[J]. NEUROCOMPUTING,2014,124(SI):131-138.
|
| APA |
Lu, Xiaoqiang,&Yuan, Yuan.(2014).Hybrid structure for robust dimensionality reduction.NEUROCOMPUTING,124(SI),131-138.
|
| MLA |
Lu, Xiaoqiang,et al."Hybrid structure for robust dimensionality reduction".NEUROCOMPUTING 124.SI(2014):131-138.
|
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