Nuclear Norm-Based 2DLPP for Image Classification | |
Lu, Yuwu1; Yuan, Chun1; Lai, Zhihui2,3; Li, Xuelong4; Wong, Wai Keung3; Zhang, David5 | |
作者部门 | 光学影像学习与分析中心 |
2017-11-01 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
卷号 | 19期号:11页码:2391-2403 |
产权排序 | 4 |
摘要 | Two-dimensional locality preserving projections (2DLPP) that use 2D image representation in preserving projection learning can preserve the intrinsic manifold structure and local information of data. However, 2DLPP is based on the Euclidean distance, which is sensitive to noise and outliers in data. In this paper, we propose a novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP). First, NN-2DLPP recovers the noisy data matrix through low-rank learning. Second, noise in data is removed and the learned clean data points are projected on a new subspace. Without the disturbance of noise, data points belonging to the same class are kept as close to each other as possible in the new projective subspace. Experimental results on six public image databases with face recognition, object classification, and handwritten digit recognition tasks demonstrated the effectiveness of the proposed method. |
文章类型 | Article |
关键词 | Image Classification Preserving Projections Robust Two-dimensional |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TMM.2017.2703130 |
收录类别 | SCI ; EI |
关键词[WOS] | NEIGHBORHOOD PRESERVING PROJECTION ; NONLINEAR DIMENSIONALITY REDUCTION ; CANONICAL CORRELATION-ANALYSIS ; PRINCIPAL COMPONENT ANALYSIS ; FACE RECOGNITION ; DISCRIMINANT-ANALYSIS ; L1-NORM MAXIMIZATION ; 2-DIMENSIONAL PCA ; REGULARIZATION ; REPRESENTATION |
语种 | 英语 |
WOS研究方向 | Computer Science ; Telecommunications |
项目资助者 | Natural Science Foundation of China(61602270 ; China Postdoctoral Science Foundation(2016M590100 ; National High Technology Research and Development Plan (863 Plan)(2011AA01A205) ; 61375012 ; 2016M590812) ; 61573248 ; 61761130079 ; U1433112) |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:000413068200003 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29370 |
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.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China 3.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China 4.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China 5.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,Yuan, Chun,Lai, Zhihui,et al. Nuclear Norm-Based 2DLPP for Image Classification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2017,19(11):2391-2403. |
APA | Lu, Yuwu,Yuan, Chun,Lai, Zhihui,Li, Xuelong,Wong, Wai Keung,&Zhang, David.(2017).Nuclear Norm-Based 2DLPP for Image Classification.IEEE TRANSACTIONS ON MULTIMEDIA,19(11),2391-2403. |
MLA | Lu, Yuwu,et al."Nuclear Norm-Based 2DLPP for Image Classification".IEEE TRANSACTIONS ON MULTIMEDIA 19.11(2017):2391-2403. |
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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 | ||
Nuclear Norm-Based 2(1415KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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