A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
- Resource Type
- Conference
- Authors
- Li, Jinli; Yuan, Ye
- Source
- 2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Networking, Sensing and Control (ICNSC), 2022 IEEE International Conference on. :1-6 Dec, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Adaptation models
Analytical models
Computational modeling
Stochastic processes
Predictive models
Prediction algorithms
Data models
High-dimensional and incomplete Data
Latent Factor Analysis
Stochastic Gradient Descent
non-linear Proportional Integral Derivation
Particle Swarm Optimization
Parameter Adaptation
- Language
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.