| Projective robust nonnegative factorization | |
Lu, Yuwu1; Lai, Zhihui2; Xu, Yong3; You, Jane4; Li, Xuelong5 ; Yuan, Chun1
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-10-10 | |
| 发表期刊 | INFORMATION SCIENCES
 |
| ISSN | 0020-0255 |
| 卷号 | 364页码:16-32 |
| 产权排序 | 5 |
| 摘要 | Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature extraction. However, both original NMF and PNMF are sensitive to noise and are unsuitable for feature extraction if data is grossly corrupted. In order to improve the robustness of NMF, a framework named Projective Robust Nonnegative Factorization (PRNF) is proposed in this paper for robust image feature extraction and classification. Since learned projections can weaken noise disturbances, PRNF is more suitable for classification and feature extraction. In order to preserve the geometrical structure of original data, PRNF introduces a graph regularization term which encodes geometrical structure. In the PRNF framework, three algorithms are proposed that add a sparsity constraint on the noise matrix based on L-1/2 norm, L-1 norm, and L-2,L-1 norm, respectively. Robustness and classification performance of the three proposed algorithms are verified with experiments on four face image databases and results are compared with state-of-the-art robust NMF-based algorithms. Experimental results demonstrate the robustness and effectiveness of the algorithms for image classification and feature extraction. (C) 2016 Elsevier Inc. All rights reserved. |
| 文章类型 | Article |
| 关键词 | Robust Nonnegative Matrix Factorization Graph Regularization Face Recognition |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.ins.2016.05.001 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | MATRIX FACTORIZATION ; IMAGE REPRESENTATION ; ALGORITHM ; PARTS |
| 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) ; China Postdoctoral Science Foundation(2016M590100) ; Shenzhen Municipal Science and Technology Innovation Council(JCYJ20130329151843309 ; 61375012 ; JCYJ20140904154630436 ; 61362031 ; JCYJ20150330155220591) ; 61300032 ; 61170253 ; U1433112) |
| WOS类目 | Computer Science, Information Systems |
| WOS记录号 | WOS:000378969400002 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28168 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Tsinghua Univ, Tsinghua CUHK Joint Res Ctr Media Sci Technol & S, Grad Sch Shenzhen, Beijing, Peoples R China 2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China 3.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Harbin, Peoples R China 4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China 5.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Yuwu,Lai, Zhihui,Xu, Yong,et al. Projective robust nonnegative factorization[J]. INFORMATION SCIENCES,2016,364:16-32. |
| APA | Lu, Yuwu,Lai, Zhihui,Xu, Yong,You, Jane,Li, Xuelong,&Yuan, Chun.(2016).Projective robust nonnegative factorization.INFORMATION SCIENCES,364,16-32. |
| MLA | Lu, Yuwu,et al."Projective robust nonnegative factorization".INFORMATION SCIENCES 364(2016):16-32. |
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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 | ||
| Projective robust no（1389KB） | 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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