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Multi-View Matrix Factorization for Sparse Mobile Crowdsensing

文献类型：期刊论文

作者Li, Xiaocan4; Xie, Kun4; Xie, Gaogang3; Li, Kenli4; Cao, Jiannong2; Zhang, Dafang4; Wen, Jigang1
刊名IEEE INTERNET OF THINGS JOURNAL
出版日期2022-12-15
卷号9期号:24页码:25767-25779
关键词Sparse matrices Sensors Data models Estimation Indexes Air quality Task analysis Matrix factorization mobile crowdsensing (MCS)
ISSN号2327-4662
DOI10.1109/JIOT.2022.3198081
英文摘要Mobile crowdsensing (MCS) has become a new paradigm for the environment sensing. However, the sparse sensory data prevent the practical and large-scale deployment of MCS systems. Recent studies have demonstrated that the matrix factorization is an effective technique which can estimate the missing sensory data entries based on a small set of observed data entries. However, there could be multiple sensory data sets with each regarded as a different view on the environment. Applying current matrix factorization individually to each data set, the recovery performance will be low as some data sets do not have enough observed data entries thus enough information. By partitioning the parameters involved in matrix factorization, we design some novel regularizations to encode the similarities among different data sets and specific knowledge in the single data set. Based on the regularizations, we propose one basic multiview matrix factorization (MVMF) model and one neural MVMF (NMVMF) model to combine multiple sensory data sets to mutually reinforce the estimation of each single data set. The extensive experimental results demonstrate that, with the help of other data sets, our models can estimate the missing entries in the data set with a very low sampling ratio accurately while the other five baseline algorithms cannot.
资助项目National ScienceFoundation for Distinguished Young Scholars[62025201] ; National Natural Science Foundation of China[62102138] ; National Natural Science Foundation of China[61972144] ; National Natural Science Foundation of China[61976087] ; China NationalPostdoctoral Program for Innovative Talents[BX20200120] ; China Postdoctoral Science Foundation[2020M682556] ; Hunan Provincial Natural Science Foundation of China[2021JJ40115] ; Huawei Innovation Project[TC20201201003]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000895792600083
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/20199]  
专题中国科学院计算技术研究所期刊论文
通讯作者Xie, Kun
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China
2.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
3.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100045, Peoples R China
4.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Peoples R China
推荐引用方式
GB/T 7714		 Li, Xiaocan,Xie, Kun,Xie, Gaogang,et al. Multi-View Matrix Factorization for Sparse Mobile Crowdsensing[J]. IEEE INTERNET OF THINGS JOURNAL,2022,9(24):25767-25779.
APA Li, Xiaocan.,Xie, Kun.,Xie, Gaogang.,Li, Kenli.,Cao, Jiannong.,...&Wen, Jigang.(2022).Multi-View Matrix Factorization for Sparse Mobile Crowdsensing.IEEE INTERNET OF THINGS JOURNAL,9(24),25767-25779.
MLA Li, Xiaocan,et al."Multi-View Matrix Factorization for Sparse Mobile Crowdsensing".IEEE INTERNET OF THINGS JOURNAL 9.24(2022):25767-25779.

入库方式： OAI收割

来源：计算技术研究所

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