Hierarchical Neighbors Embedding
文献类型：期刊论文
| 作者 | Liu, Shenglan3; Yu, Yang3 ; Liu, Kaiyuan3; Wang, Feilong2; Wen, Wujun3; Qiao, Hong1
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2022-11-21 |
| 页码 | 14 |
| ISSN号 | 2162-237X |
| 关键词 | Data sparsity hierarchical neighbors manifold learning topological and geometrical properties. the introduced to |
| 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. |
| WOS关键词 | NONLINEAR DIMENSIONALITY REDUCTION ; MANIFOLD ; PERFORMANCE ; EIGENMAPS ; SPACE ; LLE |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。