Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint

doi:10.1007/978-3-030-60239-0_23

	Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint
	Wang, Jikui1 ; Shi, Quanfu2 ; Yang, Zhengguo1 ; Nie, Feiping3
	2020
会议名称	20th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2020
会议录名称	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷号	12453 LNCS
页码	337-351
会议日期	2020-10-02 - 2020-10-04
会议地点	New York, NY, United states
出版地	CHAM
出版者	Springer Science and Business Media Deutschland GmbH
摘要	For clustering high-dimensional data, most of the state-of-the-art algorithms often extract principal component beforehand, and then conduct a concrete clustering method. However, the two-stage strategy may deviate from assignments by directly optimizing the unified objective function. Different from the traditional methods, we propose a novel method referred to as clustering by unified principal component analysis and fuzzy c-means (UPF) for clustering high-dimensional data. Our model can explore underlying clustering structure in low-dimensional space and finish clustering simultaneously. In particular, we impose a L0-norm constraint on the membership matrix to make the matrix more sparse. To solve the model, we propose an effective iterative optimization algorithm. Extensive experiments on several benchmark data sets in comparison with two-stage algorithms are conducted to validate effectiveness of the proposed method. The experiments results demonstrate that the performance of our proposed method is superiority. © 2020, Springer Nature Switzerland AG.
关键词	Cluster analysis Fuzzy systems Iterative methods Matrix algebra Parallel architectures High dimensional data Iterative optimization algorithms Low-dimensional spaces Objective functions Principal Components Sparsity constraints State-of-the-art algorithms Two-stage algorithm
DOI	10.1007/978-3-030-60239-0_23
收录类别	EI ; CPCI-S
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000719292600023
EI入藏号	20204309372689
原始文献类型	Proceedings Paper
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/9672
专题	信息工程与人工智能学院党委研究生工作部(学位管理与研究生工作处)
作者单位	1.College of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou; 2.Gansu; 3.730020, China; 4.Degree Management and Graduate Office, Lanzhou University of Finance and Economics, Lanzhou; 5.Gansu; 6.730020, China; 7.School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xian; 8.Shaanxi; 9.710072, China
推荐引用方式 GB/T 7714	Wang, Jikui,Shi, Quanfu,Yang, Zhengguo,et al. Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint[C]. CHAM:Springer Science and Business Media Deutschland GmbH,2020:337-351.