A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
This work addresses a bottleneck in industrial data analysis by enhancing latent factor models for better performance on high-dimensional incomplete data, though it appears incremental as it builds on existing methods.
The paper tackles the slow convergence of SGD-based latent factor models for high-dimensional incomplete data by proposing a Nonlinear PID-enhanced Adaptive Latent Factor (NPALF) model, which improves convergence rate and prediction accuracy on four datasets compared to five state-of-the-art models.
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.