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CSpace
A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection
Wu, Di1,2; He, Yi3; Luo, Xin1,2,4; Zhou, MengChu5,6,7
2021-08-03
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.
关键词Big data computational intelligence latent factor analysis (LFA) missing data online algorithm online feature selection sparse streaming feature streaming feature
DOI10.1109/TSMC.2021.3096065
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
页码15
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000732087500001
语种英语