Learning a Nonnegative Sparse Graph for Linear Regression
文献类型：期刊论文
| 作者 | Fang, Xiaozhao1; Xu, Yong1,2; Li, Xuelong3 ; Lai, Zhihui1,4; Wong, Wai Keung5,6
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| 刊名 | ieee transactions on image processing
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| 出版日期 | 2015-09-01 |
| 卷号 | 24期号:9页码:2760-2771 |
| 关键词 | Graph learning linear regression label propagation semi-supervised classification |
| 英文摘要 | previous graph-based semisupervised learning (g-ssl) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. to this end, a novel nonnegative sparse graph (nnsg) learning method was first proposed. then, both the label prediction and projection learning were integrated into linear regression. finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. therefore, a novel method, named learning a nnsg for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. in the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. an effective algorithm was designed to solve the corresponding optimization problem with fast convergence. furthermore, nnsg provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. the experimental results showed that nnsg can obtain very high classification accuracy and greatly outperforms conventional g-ssl methods, especially some conventional graph construction methods. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | dimensionality reduction ; discriminant-analysis ; component analysis ; face recognition ; framework ; representation ; integration ; models |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000355760300001 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/25068]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Harbin Inst Technol, Biocomp Res Ctr, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China 2.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China 3.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China 5.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China 6.Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Learning a Nonnegative Sparse Graph for Linear Regression[J]. ieee transactions on image processing,2015,24(9):2760-2771. |
| APA | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,Lai, Zhihui,&Wong, Wai Keung.(2015).Learning a Nonnegative Sparse Graph for Linear Regression.ieee transactions on image processing,24(9),2760-2771. |
| MLA | Fang, Xiaozhao,et al."Learning a Nonnegative Sparse Graph for Linear Regression".ieee transactions on image processing 24.9(2015):2760-2771. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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