中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection

文献类型：期刊论文

作者Wu, Di1,2; He, Yi3; Luo, Xin1,2,4; Zhou, MengChu5,6,7
刊名IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
出版日期2021-08-03
页码15
关键词Big data computational intelligence latent factor analysis (LFA) missing data online algorithm online feature selection sparse streaming feature streaming feature
ISSN号2168-2216
DOI10.1109/TSMC.2021.3096065
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances.
资助项目National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[62002337] ; National Natural Science Foundation of China[61902370] ; Natural Science Foundation of Chongqing, China[cstc2019jcyjmsxmX0578] ; Natural Science Foundation of Chongqing, China[cstc2019jcyjjqX0013] ; Chinese Academy of Sciences Light of West China Program ; Technology Innovation and Application Development Project of Chongqing, China[cstc2018jszx-cyzdX0041] ; Technology Innovation and Application Development Project of Chongqing, China[cstc2019jscx-fxydX0027] ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ-2020004B] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000732087500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14773]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin
作者单位1.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
3.Old Dominion Univ, Dept Comp Sci, Norfolk, VA 23462 USA
4.Cloudwalk, Dept Big Data Anal Tech, Hengrui Chongqing Artificial Intelligence Res Ctr, Chongqing 401331, Peoples R China
5.New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA
6.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
7.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Di,He, Yi,Luo, Xin,et al. A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021:15.
APA Wu, Di,He, Yi,Luo, Xin,&Zhou, MengChu.(2021).A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,15.
MLA Wu, Di,et al."A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021):15.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。