Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection
文献类型:期刊论文
作者 | Zhu, Xiaofeng1; Li, Xuelong2; Zhang, Shichao3; Ju, Chunhua3; Wu, Xindong4 |
刊名 | ieee transactions on neural networks and learning systems |
出版日期 | 2017-06-01 |
卷号 | 28期号:6页码:1263-1275 |
ISSN号 | 2162-237x |
关键词 | Dimensionality reduction manifold learning regression sparse coding |
通讯作者 | zhang, sc (reprint author), zhejiang gongshang univ, sch comp sci & informat technol, hangzhou 310018, zhejiang, peoples r china. |
产权排序 | 2 |
英文摘要 | in this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. to do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (jgsc) model. in jgsc, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. furthermore, we extend jgsc to a robust jgsc (rjgsc) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. finally, experimental results on real data sets showed that both jgsc and rjgsc outperformed the state-of-the-art algorithms in terms of k-nearest neighbor classification performance. |
学科主题 | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
WOS标题词 | science & technology ; technology |
类目[WOS] | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
研究领域[WOS] | computer science ; engineering |
关键词[WOS] | support vector machines ; image ; classification ; regression ; framework ; perspective ; cancer |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000401982100002 |
源URL | [http://ir.opt.ac.cn/handle/181661/28979] |
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.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China 2.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 3.Zhejiang Gongshang Univ, Sch Comp Sci & Informat Technol, Hangzhou 310018, Zhejiang, Peoples R China 4.Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA |
推荐引用方式 GB/T 7714 | Zhu, Xiaofeng,Li, Xuelong,Zhang, Shichao,et al. Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection[J]. ieee transactions on neural networks and learning systems,2017,28(6):1263-1275. |
APA | Zhu, Xiaofeng,Li, Xuelong,Zhang, Shichao,Ju, Chunhua,&Wu, Xindong.(2017).Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.ieee transactions on neural networks and learning systems,28(6),1263-1275. |
MLA | Zhu, Xiaofeng,et al."Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection".ieee transactions on neural networks and learning systems 28.6(2017):1263-1275. |
入库方式: OAI收割
来源:西安光学精密机械研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。