A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete DataOACSTPCDEI
A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data
High-dimensional and incomplete(HDI)matrices are primarily generated in all kinds of big-data-related practical applications.A latent factor analysis(LFA)model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO)to meet scalable requirements.However,conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model.To address this thorny issue,this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability,thereby building a state-migration particle swarm optimizer(SPSO),whose theoretical convergence is rigor-ously proved in this study.It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss.Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence,SPSO's use ensures efficient and reliable hyper-parame-ter adaptation in an LFA model,thus ensuring practicality and accurate representation learning for HDI matrices.
Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou
College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,ChinaDepartment of Computer Science,Columbia University,New York 10027 USACollege of Computer and Information Science,Southwest University,Chongqing 400715,ChinaDepartment of Electrical and Computer Engineering,Faculty of Engineering||K.A.CARE Energy Research and Innovation Center,King Abdulaziz University,Jeddah 21589,Saudi ArabiaCenter of Research Excellence in Renewable Energy and Power Systems,the Department of Electrical and Computer Engineering,Faculty of Engineering||K.A.CARE Energy Research and Innovation Center,King Abdulaziz University,Jeddah 21589,Saudi ArabiaDepartment of Electrical and Computer Engineering,New Jersey Institute of Technology,Newark,NJ 07102 USA
Data sciencegeneralized momentumhigh-dimen-sional and incomplete(HDI)datahyper-parameter adaptationlatent factor analysis(LFA)particle swarm optimization(PSO)
《自动化学报（英文版）》 2024 (011)
2220-2235 / 16
This work was supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337),the Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069),and the Deanship of Scientific Research at King Abdulaziz University,Jeddah,Saudi Arabia(RG-12-135-43).
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