.png){kind=icon}
返回
An Aggregative High-Order Singular Value Decomposition Method in Edge Computing
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2977249.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However, at present, many edge computing systems regard the two HOSVD procedures as two independent parts, which leads to repeated calculations in the cloud. In this paper, we propose an aggregative HOSVD method by reusing intermediate results of subtensor HOSVD in ECNs to reduce the HOSVD computing cost of the merged tensor in the cloud server. First, two equivalence lemmas of singular value decomposition (SVD) are proven. Second, the SVD left mergence algorithm (SVDLM), hierarchical-SVDLM (H-SVDLM) algorithm and aggregative HOSVD (AHOSVD) algorithm are proposed to obtain the HOSVD result of the merged tensor by efficiently merging the unfolded matrix SVD results of the subtensors. Finally, the efficiency and accuracy between the HOSVD and the AHOSVD are compared, and the experimental results validate that the proposed AHOSVD algorithm improves efficiency compared with the HOSVD algorithm in circumstances with comparable accuracy results with the HOSVD algorithm.
Keyword:
Tensile stress
Cloud computing
Edge computing
Data models
Singular value decomposition
Data mining
Computational modeling
Tensor
high-order singular value decomposition
edge computing
Internet of Things
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Contributions of Valine-292 in the Nicotinamide Binding Site of Liver Alcohol Dehydrogenase and Dynamics to Catalysis,.png)
Biochemistry
BiochemistryIF0
PREAI
分享收藏分享收藏
查看更多内容