Fast anchor graph optimized projections with principal component analysis and entropy regularization

doi:10.1016/j.ins.2024.121797

	Fast anchor graph optimized projections with principal component analysis and entropy regularization
	Wang, Jikui1 ; Zhang, Cuihong1 ; Zhao, Wei1 ; Huang, Xueyan1 ; Nie, Feiping2
	2025-05
在线发表日期	2024-12
发表期刊	Information Sciences
卷号	699
摘要	Traditional machine learning algorithms often fail when dealing with high-dimensional data, which is called "curse of dimensionality". In order to solve this problem, many dimensionality reduction algorithms have been proposed. Graph-based dimensionality reduction technology is a research hotspot. Traditional graph-based dimensionality reduction algorithms are based on similarity graphs and have a high time complexity of O(n2d), where n represents the number of samples and d represents the number of features. On the other hand, these methods do not consider the global data information. To solve the above two problems, we propose a novel method named Fast Anchor Graph optimized projections with Principal component analysis and Entropy regularization (FAGPE) which integrates anchor graph, entropy regularization term, and Principal Component Analysis (PCA). In the proposed model, the anchor graph with sparse constraint captures the cluster structure of the data, while the embedded Principal Component Analysis takes into account the global data information. This paper introduces a novel iterative optimization approach to address the proposed model. In general, the time complexity of our proposed algorithm is O(nmd), with m representing the number of anchors. Finally, the experimental results on many benchmark data sets show that the proposed algorithm achieves better classification performance on the reduced dimension data. © 2024 Elsevier Inc.
关键词	Contrastive Learning Dimensionality reduction Principal component analysis Data informations Dimensionality reduction Dimensionality reduction algorithms Entropy regularization Global data Graph-based Machine learning algorithms Principal-component analysis Regularisation Time complexity
DOI	10.1016/j.ins.2024.121797
收录类别	EI ; SCIE
ISSN	0020-0255
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001407346600001
出版者	Elsevier Inc.
EI入藏号	20245217596324
EI主题词	Adversarial machine learning
EI分类号	1101.2
原始文献类型	Journal article (JA)
EISSN	1872-6291
引用统计	被引频次：1[WOS] [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/38654
专题	信息工程与人工智能学院
通讯作者	Wang, Jikui
作者单位	1.College 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, Shanxi, Xi'an; 710072, China
第一作者单位	信息工程与人工智能学院
通讯作者单位	信息工程与人工智能学院
推荐引用方式 GB/T 7714	Wang, Jikui,Zhang, Cuihong,Zhao, Wei,et al. Fast anchor graph optimized projections with principal component analysis and entropy regularization[J]. Information Sciences,2025,699.
APA	Wang, Jikui,Zhang, Cuihong,Zhao, Wei,Huang, Xueyan,&Nie, Feiping.(2025).Fast anchor graph optimized projections with principal component analysis and entropy regularization.Information Sciences,699.
MLA	Wang, Jikui,et al."Fast anchor graph optimized projections with principal component analysis and entropy regularization".Information Sciences 699(2025).