Structure preserved fast dimensionality reduction

doi:10.1016/j.asoc.2024.111817

	Structure preserved fast dimensionality reduction
	Yi, Jihai1 ; Duan, Huiyu1 ; Wang, Jikui1 ; Yang, Zhengguo1 ; Nie, Feiping2
	2024-09
发表期刊	Applied Soft Computing
卷号	162
摘要	Many graph-based unsupervised dimensionality reduction techniques have raised concerns about their high accuracy. However, there is an urgent need to address the enormous time consumption problem in large-scale data scenarios. Therefore, we present a novel approach named Structure Preserved Fast Dimensionality Reduction (SPFDR). Firstly, the parameter-insensitive, sparse, and scalable bipartite graph is constructed to build the similarity matrix. Then, employing alternating iterative optimization, the linear dimensionality reduction matrix and the optimal similarity matrix preserved cluster structure are learned. The computational complexity of the conventional graph-based dimension reduction method costs O(n2d+d3), yet the proposed approach is O(ndm+nm2), wherein n, m, and d are the number of instances, anchors, and features, respectively. Eventually, experiments conducted with multiple open datasets will provide convincing evidence for how effective and efficient the proposed method is. © 2024 Elsevier B.V.
关键词	Data reduction Graph theory Graphic methods Matrix algebra Bipartite graphs Dimensionality reduction Dimensionality reduction techniques Graph-based High-accuracy Iterative Optimization Large scale data Linear dimensionality reduction Similarity matrix Time consumption
DOI	10.1016/j.asoc.2024.111817
收录类别	EI
ISSN	1568-4946
语种	英语
出版者	Elsevier Ltd
EI入藏号	20242516288341
EI主题词	Iterative methods
EI分类号	723.2 Data Processing and Image Processing ; 921.1 Algebra ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory ; 921.6 Numerical Methods
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/37282
专题	信息工程与人工智能学院
通讯作者	Yang, Zhengguo
作者单位	1.School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Gansu, Lanzhou; 730020, China; 2.School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Shaanxi, Xi'an; 710072, China
第一作者单位	兰州财经大学
通讯作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Yi, Jihai,Duan, Huiyu,Wang, Jikui,et al. Structure preserved fast dimensionality reduction[J]. Applied Soft Computing,2024,162.
APA	Yi, Jihai,Duan, Huiyu,Wang, Jikui,Yang, Zhengguo,&Nie, Feiping.(2024).Structure preserved fast dimensionality reduction.Applied Soft Computing,162.
MLA	Yi, Jihai,et al."Structure preserved fast dimensionality reduction".Applied Soft Computing 162(2024).