Multi-View Matrix Factorization for Sparse Mobile Crowdsensing

doi:10.1109/JIOT.2022.3198081

	Multi-View Matrix Factorization for Sparse Mobile Crowdsensing
	Li, Xiaocan 1; Xie, Kun 1; Xie, Gaogang 2; Li, Kenli 1; Cao, Jiannong 3; Zhang, Dafang 1; Wen, Jigang 4
	2022-12-15
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
卷号	9 期号:24 页码:25767-25779
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.
关键词	Sparse matrices Sensors Data models Estimation Indexes Air quality Task analysis Matrix factorization mobile crowdsensing (MCS)
DOI	10.1109/JIOT.2022.3198081
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000895792600083
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20200
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.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Peoples R China 2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100045, Peoples R China 3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, 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.