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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
Y. Yuan, S. Lu, and X. Luo, “A proportional integral controller-enhanced non-negative latent factor analysis model,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–14, Jun. 2025.
Citation: Y. Yuan, S. Lu, and X. Luo, “A proportional integral controller-enhanced non-negative latent factor analysis model,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1–14, Jun. 2025.
shu

A Proportional Integral Controller-Enhanced Non-Negative Latent Factor Analysis Model

Funds:  This work was supported in part by the National Natural Science Foundation of China (62372385, 62272078) and the Chongqing Natural Science Foundation (CSTB2023NSCQ-LZX0069)
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    Abstract
  • A non-negative latent factor (NLF) model is able to be built efficiently via a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm for performing precise representation to high-dimensional and incomplete (HDI) matrix from many kinds of big-data-related applications. However, an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information, making a resultant model suffer from slow convergence. To address this issue, this study proposes a proportional integral (PI) controller-enhanced NLF (PI-NLF) model with two-fold ideas: 1) Designing an increment refinement (IR) mechanism, which formulates the current and past update increments as the proportional and integral terms of a PI controller, thereby assimilating the past update information into the learning scheme smoothly with high efficiency; 2) Deriving an IR-based SLF-NMU (ISN) algorithm, which updates a latent factor following the principle of an IR mechanism, thus significantly accelerating an NLF model’s convergence rate. The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix. The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system, social network, and cloud service system. The code is available at https://github.com/yuanyeswu/PINLF/blob/main/PINLF-code.zip.

     

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