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A generalized multi-dictionary least squares framework regularized with multi-graph embeddings

文献类型：期刊论文

作者Abeo, Timothy Apasiba2,3; Shen, Xiang-Jun2; Bao, Bing-Kun4; Zha, Zheng-Jun1; Fan, Jianping5
刊名PATTERN RECOGNITION
出版日期2019-06-01
卷号90页码:1-11
关键词Multi-view dimension reduction Least squares Multiple graphs Feature extraction Classification
ISSN号0031-3203
DOI10.1016/j.patcog.2019.01.012
通讯作者Shen, Xiang-Jun(xjshen@ujs.edu.cn)
英文摘要Dimensionality reduction in high dimensional multi-view datasets is an important research topic. It can keep essential features to improve performance in subsequent tasks such as classification and clustering. This paper proposes a generalized framework, which extends the PCA idea of minimizing least squares reconstruction errors, to include data distribution and multiple dictionaries for preserving outliers-free global structures in multi-view datasets. To also preserve local manifold structures, multiple local graphs are incorporated. Finally two models, in Multi-dictionary Least Squares Framework regularized with Multi-graph Embeddings (MD-MGE), are proposed for preserving both global and local structures. Extensive experimental results on four multi-view datasets prove both methods outperform the existing comparative methods. Also, their accuracy rates improvements are statistically significant on all cases below the significance level of 0.05. (C) 2019 Elsevier Ltd. All rights reserved.
WOS关键词CANONICAL CORRELATION-ANALYSIS ; LINEAR DISCRIMINANT-ANALYSIS ; DIMENSIONALITY REDUCTION ; PRESERVING PROJECTIONS ; CLASSIFICATION ; INFORMATION ; EXTENSIONS
资助项目National Natural Science Foundation of China[61572240] ; National Natural Science Foundation of China[61622211] ; National Natural Science Foundation of China[61472392] ; National Natural Science Foundation of China[61620106009] ; Beijing Natural Science Foundation[4152053]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000463130400001
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/28069]  
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
作者单位1.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei, Anhui, Peoples R China
2.JiangSu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Jiangsu, Peoples R China
3.Tamale Tech Univ, Sch Appl Sci, Box 3ER, Tamale, Ghana
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
5.Univ North Carolina Charlotte, Dept Comp Sci, Charlotte, NC 28223 USA
推荐引用方式
GB/T 7714		 Abeo, Timothy Apasiba,Shen, Xiang-Jun,Bao, Bing-Kun,et al. A generalized multi-dictionary least squares framework regularized with multi-graph embeddings[J]. PATTERN RECOGNITION,2019,90:1-11.
APA Abeo, Timothy Apasiba,Shen, Xiang-Jun,Bao, Bing-Kun,Zha, Zheng-Jun,&Fan, Jianping.(2019).A generalized multi-dictionary least squares framework regularized with multi-graph embeddings.PATTERN RECOGNITION,90,1-11.
MLA Abeo, Timothy Apasiba,et al."A generalized multi-dictionary least squares framework regularized with multi-graph embeddings".PATTERN RECOGNITION 90(2019):1-11.

入库方式： OAI收割

来源：自动化研究所

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