Orthogonal self-guided similarity preserving projection for classification and clustering | |
Fang, Xiaozhao1; Xu, Yong2; Li, Xuelong3; Lai, Zhihui4; Teng, Shaohua1; Fei, Lunke2 | |
刊名 | neural networks |
2017-04-01 | |
卷号 | 88页码:1-8 |
关键词 | Dimensionality reduction Intrinsic structure Subspace clustering Feature representation |
ISSN号 | 0893-6080 |
通讯作者 | xu, y (reprint author), harbin inst technol, shenzhen grad sch, biocomp res ctr, shenzhen 518055, guangdong, peoples r china. |
产权排序 | 3 |
英文摘要 | a suitable feature representation can faithfully preserve the intrinsic structure of data. however, traditional dimensionality reduction (dr) methods commonly use the original input features to define the intrinsic structure, which makes the estimated intrinsic structure unreliable since redundant or noisy features may exist in the original input features. thus a dilemma is that (1) one needs the most suitable feature representation to define the intrinsic structure of data and (2) one should use the proper intrinsic structure of data to perform feature extraction. to address the problem, in this paper we propose a unified learning framework to simultaneously obtain the optimal feature representation and intrinsic structure of data. the structure is learned from the results of feature learning, and the features are learned to preserve the refined structure of data. by leveraging the interactions between the process of determining the most suitable feature representation and intrinsic structure of data, we can capture accurate structure and obtain the optimal feature representation of data. experimental results demonstrate that our method outperforms state-of-the-art methods in dr and subspace clustering. the code of the proposed method is available at "http://www.yongxu.org/lunwen.html''. (c) 2017 elsevier ltd. all rights reserved. |
学科主题 | computer science, artificial intelligence ; neurosciences |
WOS标题词 | science & technology ; technology ; life sciences & biomedicine |
类目[WOS] | computer science, artificial intelligence ; neurosciences |
研究领域[WOS] | computer science ; neurosciences & neurology |
关键词[WOS] | principal component analysis ; sparse graph ; recognition ; regression ; framework ; 2dpca |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000397959900001 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/28894] |
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.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China 2.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China 3.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China 4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Guangdong, Peoples R China |
推荐引用方式 GB/T 7714 | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Orthogonal self-guided similarity preserving projection for classification and clustering[J]. neural networks,2017,88:1-8. |
APA | Fang, Xiaozhao,Xu, Yong,Li, Xuelong,Lai, Zhihui,Teng, Shaohua,&Fei, Lunke.(2017).Orthogonal self-guided similarity preserving projection for classification and clustering.neural networks,88,1-8. |
MLA | Fang, Xiaozhao,et al."Orthogonal self-guided similarity preserving projection for classification and clustering".neural networks 88(2017):1-8. |
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