Orthogonal self-guided similarity preserving projection for classification and clustering | |
Fang, Xiaozhao1; Xu, Yong2; Li, Xuelong3; Lai, Zhihui4; Teng, Shaohua1; Fei, Lunke2; Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China. | |
作者部门 | 光学影像学习与分析中心 |
2017-04-01 | |
发表期刊 | NEURAL NETWORKS |
ISSN | 0893-6080 |
卷号 | 88页码:1-8 |
产权排序 | 3 |
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. |
文章类型 | Article |
关键词 | Dimensionality Reduction Intrinsic Structure Subspace Clustering Feature Representation |
学科领域 | Computer Science, Artificial Intelligence |
WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
DOI | 10.1016/j.neunet.2017.01.001 |
收录类别 | SCI ; EI |
关键词[WOS] | PRINCIPAL COMPONENT ANALYSIS ; SPARSE GRAPH ; RECOGNITION ; REGRESSION ; FRAMEWORK ; 2DPCA |
语种 | 英语 |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
项目资助者 | National Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61370163 ; 61332011) |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:000397959900001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28894 |
专题 | 光谱成像技术研究室 |
通讯作者 | Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China. |
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.,...&Xu, Y .(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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Orthogonal self-guid(813KB) | 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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