Hierarchical Neighbors Embedding

doi:10.1109/TNNLS.2022.3221103

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Hierarchical Neighbors Embedding
	Liu, Shenglan 3; Yu, Yang 3; Liu, Kaiyuan 3; Wang, Feilong 2; Wen, Wujun 3; Qiao, Hong 1
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2022-11-21
页码	14
关键词	Data sparsity hierarchical neighbors manifold learning topological and geometrical properties. the introduced to
ISSN号	2162-237X
DOI	10.1109/TNNLS.2022.3221103
通讯作者	Liu, Shenglan(liusl@mail.dlut.edu.cn)
英文摘要	Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observation space. To address this issue, in this article, we propose hierarchical neighbors embedding (HNE), which enhances the local connections through hierarchical combination of neighbors. And three different HNE-based implementations are derived by further analyzing the topological connection and reconstruction performance. The experimental results on both the synthetic and real-world datasets illustrate that our HNE-based methods could obtain more faithful embeddings with better topological and geometrical properties. From the view of embedding quality, HNE develops the outstanding advantages in dealing with data of general distributions. Furthermore, comparing with other state-of-the-art manifold learning methods, HNE shows its superiority in dealing with sparsely sampled data and weak-connected manifolds.
资助项目	Major Project of Science and Technology Innovation 2030 C Brain Scienceand Brain-Inspired Intelligence[2021ZD0200408] ; National Natural Science Foundation of China[91948303] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; Fundamental Research Fundsfor the Central Universities[DUT22JC14]
WOS关键词	NONLINEAR DIMENSIONALITY REDUCTION ; MANIFOLD ; PERFORMANCE ; EIGENMAPS ; SPACE ; LLE
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000890866000001
资助机构	Major Project of Science and Technology Innovation 2030 C Brain Scienceand Brain-Inspired Intelligence ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; Fundamental Research Fundsfor the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50785]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Liu, Shenglan
作者单位	1.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116024, Peoples R China 3.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shenglan,Yu, Yang,Liu, Kaiyuan,et al. Hierarchical Neighbors Embedding[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:14.
APA	Liu, Shenglan,Yu, Yang,Liu, Kaiyuan,Wang, Feilong,Wen, Wujun,&Qiao, Hong.(2022).Hierarchical Neighbors Embedding.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA	Liu, Shenglan,et al."Hierarchical Neighbors Embedding".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):14.