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A generalized least-squares approach regularized with graph embedding for dimensionality reduction

文献类型：期刊论文

作者Shen, Xiang-Jun2; Liu, Si-Xing2; Bao, Bing-Kun3; Pan, Chun-Hong4; Zha, Zheng-Jun5; Fan, Jianping1
刊名PATTERN RECOGNITION
出版日期2020-02-01
卷号98页码:10
ISSN号0031-3203
关键词Dimensionality reduction Graph embedding Subspace learning Least-squares
DOI10.1016/j.patcog.2019.107023
通讯作者Shen, Xiang-Jun(xjshen@ujs.edu.cn) ; Zha, Zheng-Jun(zhazj@ustc.edu.cn)
英文摘要In current graph embedding methods, low dimensional projections are obtained by preserving either global geometrical structure of data or local geometrical structure of data. In this paper, the PCA (Principal Component Analysis) idea of minimizing least-squares reconstruction errors is regularized with graph embedding, to unify various local manifold embedding methods within a generalized framework to keep global and local low dimensional subspace. Different from the well-known PCA method, our proposed generalized least-squares approach considers data distributions together with an instance penalty in each data point. In this way, PCA is viewed as a special instance of our proposed generalized least squares framework for preserving global projections. Applying a regulation of graph embedding, we can obtain projection that preserves both intrinsic geometrical structure and global structure of data. From the experimental results on a variety of face and handwritten digit recognition, our proposed method has advantage of superior performances in keeping lower dimensional subspaces and higher classification results than state-of-the-art graph embedding methods. (C) 2019 Elsevier Ltd. All rights reserved.
WOS关键词PRESERVING PROJECTIONS ; EIGENMAPS ; FRAMEWORK
资助项目National Natural Science Foundation of China[61572240] ; National Natural Science Foundation of China[61622211] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61572503] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[6193000388] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[201600005]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000497600300024
资助机构National Natural Science Foundation of China ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
源URL[http://ir.ia.ac.cn/handle/173211/29385]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Shen, Xiang-Jun; Zha, Zheng-Jun
作者单位1.Univ N Carolina, Dept Comp Sci, Charlotte, NC 28223 USA
2.JiangSu Univ, Sch Comp Sci & Commun Engn, Nanjing 212013, Jiangsu, Peoples R China
3.Nanjing Univ Posts & Telecommun, Nanjing, Jiangsu, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
5.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Xiang-Jun,Liu, Si-Xing,Bao, Bing-Kun,et al. A generalized least-squares approach regularized with graph embedding for dimensionality reduction[J]. PATTERN RECOGNITION,2020,98:10.
APA Shen, Xiang-Jun,Liu, Si-Xing,Bao, Bing-Kun,Pan, Chun-Hong,Zha, Zheng-Jun,&Fan, Jianping.(2020).A generalized least-squares approach regularized with graph embedding for dimensionality reduction.PATTERN RECOGNITION,98,10.
MLA Shen, Xiang-Jun,et al."A generalized least-squares approach regularized with graph embedding for dimensionality reduction".PATTERN RECOGNITION 98(2020):10.

入库方式： OAI收割

来源：自动化研究所

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