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Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation

文献类型：期刊论文

作者Feng, Xiang-Chu1; Zhao, Chen-Ping1,2; Peng, Si-Long3,4; Hu, Xi-Yuan3,4,5; Ouyang, Zhao-Wei1
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2019-04-01
卷号34期号:2页码:476-493
关键词crime density estimation augmented Lagrangian strategy Plug-and-Play filtering operator
ISSN号1000-9000
DOI10.1007/s11390-019-1920-1
通讯作者Zhao, Chen-Ping(zcp0378@163.com)
英文摘要Different from a general density estimation, the crime density estimation usually has one important factor: the geographical constraint. In this paper, a new crime density estimation model is formulated, in which the regions where crime is impossible to happen, such as mountains and lakes, are excluded. To further optimize the estimation method, a learning-based algorithm, named Plug-and-Play, is implanted into the augmented Lagrangian scheme, which involves an off-the-shelf filtering operator. Different selections of the filtering operator make the algorithm correspond to several classical estimation models. Therefore, the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical methods. In the experiment part, synthetic examples with different invalid regions and samples of various distributions are first tested. Then under complex geographic constraints, we apply the proposed method with a real crime dataset to recover the density estimation. The state-of-the-art results show the feasibility of the proposed model.
WOS关键词IMAGE DECOMPOSITION
资助项目National Natural Science Foundation of China[61772389] ; National Natural Science Foundation of China[61871260] ; Open Project of National Engineering Laboratory for Forensic Science of China[2017NELKFKT02] ; Key Scientific Research Projects in Henan Colleges and Universities of China[19A110015]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000462960800014
出版者SCIENCE PRESS
资助机构National Natural Science Foundation of China ; Open Project of National Engineering Laboratory for Forensic Science of China ; Key Scientific Research Projects in Henan Colleges and Universities of China
源URL[http://ir.ia.ac.cn/handle/173211/24950]  
专题自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者Zhao, Chen-Ping
作者单位1.Xidian Univ, Sch Math & Stat, Xian 710126, Shaanxi, Peoples R China
2.Henan Inst Sci & Technol, Sch Math Sci, Xinxiang 453003, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China
5.Beijing Visyst Co Ltd, Res Ctr, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Feng, Xiang-Chu,Zhao, Chen-Ping,Peng, Si-Long,et al. Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2019,34(2):476-493.
APA Feng, Xiang-Chu,Zhao, Chen-Ping,Peng, Si-Long,Hu, Xi-Yuan,&Ouyang, Zhao-Wei.(2019).Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,34(2),476-493.
MLA Feng, Xiang-Chu,et al."Plug-and-Play Based Optimization Algorithm for New Crime Density Estimation".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 34.2(2019):476-493.

入库方式： OAI收割

来源：自动化研究所

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