Fast anchor graph preserving projections

doi:10.1016/j.patcog.2023.109996

	Fast anchor graph preserving projections
	Wang, Jikui1,2 ; Wu, Yiwen1 ; Li, Bing 3; Yang, Zhenguo 1; Nie, Feiping4
	2024-02
在线发表日期	2023-09
发表期刊	Pattern Recognition
卷号	146
摘要	The existing graph-based dimensionality reduction algorithms need to learn an adjacency matrix or construct it in advance, therefore the time complexity of the graph-based dimensionality reduction algorithms is not less than O(n2d), where n denotes the number of samples, d denotes the number of dimensions. Moreover, the existing dimensionality reduction algorithms do not consider the cluster information in the original space, resulting in the weakening or even loss of valuable information after dimensionality reduction. To address the above problems, we propose Fast Anchor Graph Preserving Projections (FAGPP), which learns the projection matrix, the anchors and the membership matrix at the same time. Especially, FAGPP has a built-in Principal Component Analysis (PCA) item, which makes our model not only deal with the cluster information of data, but also deal with the global information of data. The time complexity of FAGPP is O(nmd), where m denotes the number of the anchors and m is much less than n. We propose a novel iterative algorithm to solve the proposed model and the convergence of the algorithm is proved theoretically. The experimental results on a large number of high-dimensional benchmark image data sets demonstrate the efficiency of FAGPP. The data sets and the source code are available from https://github.com/511lab/FAGPP. © 2023 Elsevier Ltd
关键词	Anchors Clustering algorithms Graphic methods Machine learning Matrix algebra Principal component analysis Reduction Adjacency matrix Anchor graph Dimensionality reduction Dimensionality reduction algorithms Graph-based Learn+ Number of samples Principal-component analysis Projection matrix Time complexity
DOI	10.1016/j.patcog.2023.109996
收录类别	EI ; SCIE
ISSN	0031-3203
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001159087500001
出版者	Elsevier Ltd
EI入藏号	20234014831158
EI主题词	Iterative methods
EI分类号	671.2 Ship Equipment ; 723.4 Artificial Intelligence ; 802.2 Chemical Reactions ; 903.1 Information Sources and Analysis ; 921.1 Algebra ; 921.6 Numerical Methods ; 922.2 Mathematical Statistics
原始文献类型	Journal article (JA)
EISSN	1873-5142
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/35352
专题	信息工程与人工智能学院
通讯作者	Wang, Jikui
作者单位	1.School of Information Engineering and Artificial Intelligence, Lanzhou University of Finance and Economics, Gansu, Lanzhou; 730000, China; 2.State Key Laboratory of Public Big Data, Guizhou University, Guizhou, Guiyang; 550025, China; 3.School of Management, Xiamen University, Xiamen; 361005, China; 4.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	Wang, Jikui,Wu, Yiwen,Li, Bing,et al. Fast anchor graph preserving projections[J]. Pattern Recognition,2024,146.
APA	Wang, Jikui,Wu, Yiwen,Li, Bing,Yang, Zhenguo,&Nie, Feiping.(2024).Fast anchor graph preserving projections.Pattern Recognition,146.
MLA	Wang, Jikui,et al."Fast anchor graph preserving projections".Pattern Recognition 146(2024).