论文标题
非线性PID增强自适应潜在因子分析模型
A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
论文作者
论文摘要
高维和不完整（HDI）数据在各种工业应用中具有巨大的交互信息。潜在因子（LF）模型在从具有随机梯度不错（SGD）算法的HDI数据中提取有价值的信息方面非常有效。但是，基于SGD的LFA模型患有缓慢的收敛性，因为它仅考虑当前的学习错误。为了解决这个关键问题，本文提出了一个非线性PID增强的自适应潜在因素（NPALF）模型，具有两个折叠的想法：1）通过考虑过去的学习错误，按照非线性PID控制器的原理来重建学习错误； b）遵循粒子群优化（PSO）算法的原理有效地实现所有参数适应。四个代表性HDI数据集的经验结果表明，与五个最先进的LFA模型相比，NPALF模型可实现丢失HDI数据数据的更好的收敛率和预测准确性。
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.
