A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model

• URL: http://arxiv.org/abs/2208.02513v1
• Date: Thu, 4 Aug 2022 07:48:19 GMT
• Title: A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
• Authors: Jinli Li, Ye Yuan
• Abstract summary: High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with decent gradient (SGD) algorithm. An SGD-based LFA model suffers from slow convergence since it only considers the current learning error.
• Score: 6.2303427193075755
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications. A latent factor (LF) model is remarkably effective in extracting valuable information from HDI data with stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model suffers from slow convergence since it only considers the current learning error. To address this critical issue, this paper proposes a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1) rebuilding the learning error via considering the past learning errors following the principle of a nonlinear PID controller; b) implementing all parameters adaptation effectively following the principle of a particle swarm optimization (PSO) algorithm. Experience results on four representative HDI datasets indicate that compared with five state-of-the-art LFA models, the NPALF model achieves better convergence rate and prediction accuracy for missing data of an HDI data.

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