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Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis
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DOI：10.1109/TKDE.2020.3033324.png)
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Abstract
En 中文
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization ((PSO)-S-2) algorithm. It is subsequently adopted to implement a (PSO)-S-2-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate, and it can achieve even higher prediction accuracy for missing data of an HiDS matrix. On the other hand, compared with state-of-the-art adaptive LFA models, a PLFA model's prediction accuracy and computational efficiency are highly competitive. Hence, it has high potential in addressing real industrial issues.
Keywords:
Adaptation models
Computational modeling
Heuristic algorithms
Standards
Convergence
Particle swarm optimization
Sparse matrices
Data science
computational intelligence
learning rate adaptation
hyper parameter adaptation
latent factor analysis
particle swarm optimization
high-dimensional and sparse data
adaptive algorithm
industrial application
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