A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System
文献类型：期刊论文
| 作者 | Cui, Zhihua1; Jing, Xuechun1; Zhao, Peng1; Zhang, Wensheng2 ; Chen, Jinjun3
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| 刊名 | IEEE INTERNET OF THINGS JOURNAL
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| 出版日期 | 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. |
| WOS关键词 | ALGORITHM ; SEGMENTATION ; INTERNET ; ROBUST |
| 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研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000682147600012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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) |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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