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Volume 11 Issue 11
Nov.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(11): 2220-2235
J. Chen, K. Liu, X. Luo, Y. Yuan, K. Sedraoui, Y. Al-Turki, and  M. C. Zhou,  “A state-migration particle swarm optimizer for adaptive latent factor analysis of high-dimensional and incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2220–2235, Nov. 2024. doi: 10.1109/JAS.2024.124575
Citation: J. Chen, K. Liu, X. Luo, Y. Yuan, K. Sedraoui, Y. Al-Turki, and  M. C. Zhou,  “A state-migration particle swarm optimizer for adaptive latent factor analysis of high-dimensional and incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2220–2235, Nov. 2024. doi: 10.1109/JAS.2024.124575
shu

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

doi: 10.1109/JAS.2024.124575
Funds:  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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    Abstract

  • 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 rigorously 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-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

     

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    • Jiufang Chen and Kechen Liu contributed equally to this work.
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    • An SPSO algorithm injects particles’ historical position and velocity into the evolution process, enhancing its search ability
    • The SPSO’s theoretical convergence is rigorously proved via the analyses of the stochastic convergence conditions on the particles’ position expectations
    • An SPSO-incorporated LFA model implements efficient hyper-parameter adaptation without accuracy loss
    • Experimental results evidently validate that the SPSO-incorporated LFA model can obtain better performance than the state-of-the-art models

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