A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System

doi:10.1109/JIOT.2021.3056578

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System
	Cui, Zhihua 1; Jing, Xuechun 1; Zhao, Peng 1; Zhang, Wensheng 2; Chen, Jinjun 3
刊名	IEEE INTERNET OF THINGS JOURNAL
	2021-08-15
卷号	8 期号:16 页码:12540-12549
关键词	Sparse matrices Internet of Things Clustering algorithms Correlation Artificial intelligence Servers Hyperspectral imaging Close neighbors data analysis hyperspectral images (HSIs) Internet of Things (IoT) subspace clustering
ISSN号	2327-4662
DOI	10.1109/JIOT.2021.3056578
通讯作者	Cui, Zhihua(cuizhihua@tyust.edu.cn)
英文摘要	The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and always transmitted to the cloud for analysis. In order to reduce cost and reply promptly, we deploy artificial intelligence (AI) models for data analysis on edge servers. Subspace clustering, the core of the AI model, is employed to analyze high-dimensional image data such as HSIs. However, most traditional subspace clustering algorithms construct a single model, which can be affected by noise more easily. It hardly balances the sparsity and connectivity of the representation coefficient matrix. Therefore, we proposed a postprocess strategy of subspace clustering for taking account of sparsity and connectivity. First, we define close neighbors as having more common neighbors and higher coefficients neighbors, where the close neighbors are selected according to the nondominated sorting algorithm. Second, the coefficients between the sample and close neighbors are reserved, incorrect, or useless connections are pruned. Then, the postprocess strategy can reserve the intrasubspace connection and prune the intersubspace connection. In experiments, we verified the universality and effectiveness of postprocessing strategies in the traditional image recognition field and IoT field, respectively. The experiment results demonstrate that the proposed strategy can process noise data in the IoT to improve clustering accuracy.
资助项目	National Key Research and Development Program of China[2018 YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[61772478] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Key Research and Development Program of Shanxi Province (International Cooperation)[201903D421048] ; Australian Research Council (ARC)[DP190101893] ; Australian Research Council (ARC)[DP170100136] ; Australian Research Council (ARC)[LP180100758]
WOS关键词	ALGORITHM ; SEGMENTATION ; INTERNET ; ROBUST
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000682147600012
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research and Development Program of Shanxi Province (International Cooperation) ; Australian Research Council (ARC)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45697]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Cui, Zhihua
作者单位	1.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan 030024, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China 3.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3000, Australia
推荐引用方式 GB/T 7714	Cui, Zhihua,Jing, Xuechun,Zhao, Peng,et al. A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System[J]. IEEE INTERNET OF THINGS JOURNAL,2021,8(16):12540-12549.
APA	Cui, Zhihua,Jing, Xuechun,Zhao, Peng,Zhang, Wensheng,&Chen, Jinjun.(2021).A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System.IEEE INTERNET OF THINGS JOURNAL,8(16),12540-12549.
MLA	Cui, Zhihua,et al."A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System".IEEE INTERNET OF THINGS JOURNAL 8.16(2021):12540-12549.