Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors

doi:10.1007/s10489-022-03534-z

	Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors
	Yang, Zhengguo1,2 ; Wang, Jikui1,2 ; Li, Qiang 1,2; Yi, Jihai1,2 ; Liu, Xuewen1,2 ; Nie, Feiping1,3
	2023-01
发表期刊	Applied Intelligence
卷号	53 期号:2 页码:2348-2361
摘要	Graph-based dimensionality reduction methods have attracted much attention for they can be applied successfully in many practical problems such as digital images and information retrieval. Two main challenges of these methods are how to choose proper neighbors for graph construction and make use of global and local information when conducting dimensionality reduction. In this paper, we want to tackle these two challenges by presenting an improved graph optimization approach for unsupervised dimensionality reduction. Our method can deal with dimensionality reduction and graph construction at the same time, which doesn’t need to construct an affinity graph beforehand. On the other hand, by integrating the advantages of the orthogonal local preserving projections and principal component analysis, both the local and global information of the original data are considered in dimensionality reduction in our approach. Eventually, we learn the sparse affinity graph by considering probabilistic neighbors, which is optimal and suitable for classification. To testify the superiority of our approach, we carry out some experiments on several publicly available UCI and image data sets, and the results have demonstrated the effectiveness of our approach. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
关键词	Graph algorithms Graphic methods Dimensionality reduction Dimensionality reduction method Graph construction Graph optimization Graph-based Locality preserving projections Principal-component analysis Probabilistic neighbor Probabilistics Unsupervised dimensionality reduction
DOI	10.1007/s10489-022-03534-z
收录类别	EI ; SCIE
ISSN	0924-669X
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000791661000001
出版者	Springer
EI入藏号	20221912080773
EI主题词	Principal component analysis
EI分类号	922.2 Mathematical Statistics
原始文献类型	Journal article (JA)
EISSN	1573-7497
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/34496
专题	信息工程与人工智能学院
作者单位	1.School of information engineering, Lanzhou University of Finance and Economics, Gansu, Lanzhou; 730020, China; 2.GANSU Province Key laboratory of E-business technology and application, Gansu, Lanzhou; 730020, China; 3.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Shaanxi, Xi’an; 710072, China
第一作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Yang, Zhengguo,Wang, Jikui,Li, Qiang,et al. Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors[J]. Applied Intelligence,2023,53(2):2348-2361.
APA	Yang, Zhengguo,Wang, Jikui,Li, Qiang,Yi, Jihai,Liu, Xuewen,&Nie, Feiping.(2023).Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors.Applied Intelligence,53(2),2348-2361.
MLA	Yang, Zhengguo,et al."Graph optimization for unsupervised dimensionality reduction with probabilistic neighbors".Applied Intelligence 53.2(2023):2348-2361.