| Low-Rank Preserving Projections | |
Lu, Yuwu1,2; Lai, Zhihui3,4; Xu, Yong2; Li, Xuelong5 ; Zhang, David6; Yuan, Chun1
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-08-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 卷号 | 46期号:8页码:1900-1913 |
| 产权排序 | 5 |
| 摘要 | As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L-21 norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results. |
| 文章类型 | Article |
| 关键词 | Face Recognition Image Classification Locality Preserving Projections (Lpp) Low-rank Representation (Lrr) |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2015.2457611 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; FACE-RECOGNITION ; FRAMEWORK ; GRAPH ; LDA ; LAPLACIANFACES ; ALGORITHMS ; PCA |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| 项目资助者 | Natural Science Foundation of China(61203376 ; National Significant Science and Technology Projects of China(2013ZX01039001-002-003) ; Shenzhen Municipal Science and Technology Innovation Council(JCYJ20130329151843309 ; 61300032 ; JCYJ20140904154630436) ; 61375012 ; 61362031 ; 61170253 ; 61370163) |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000379984500017 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28171 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Tsinghua Univ, Grad Sch Shenzhen, Tsinghua CUHK Joint Res Ctr Media Sci Technol & S, Shenzhen 518055, Peoples R China 2.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China 3.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China 4.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China 5.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr OPT Imagery Anal & Learning, Xian 710119, Peoples R China 6.Hong Kong Polytech Univ, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Yuwu,Lai, Zhihui,Xu, Yong,et al. Low-Rank Preserving Projections[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(8):1900-1913. |
| APA | Lu, Yuwu,Lai, Zhihui,Xu, Yong,Li, Xuelong,Zhang, David,&Yuan, Chun.(2016).Low-Rank Preserving Projections.IEEE TRANSACTIONS ON CYBERNETICS,46(8),1900-1913. |
| MLA | Lu, Yuwu,et al."Low-Rank Preserving Projections".IEEE TRANSACTIONS ON CYBERNETICS 46.8(2016):1900-1913. |
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| Low-Rank Preserving （3239KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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