论文标题
PI-NLF：一种用于非负潜在因子分析的比例综合方法
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
论文作者
论文摘要
高维和不完整（HDI）矩阵经常出现在与大数据相关的各种应用中，这表明了许多节点之间固有的非负相互作用。非负潜在因子（NLF）模型对HDI矩阵进行有效的表示学习，其学习过程主要依赖于单个潜在因子依赖性，非负和乘法更新（SLF-NMU）算法。但是，SLF-NMU算法仅根据当前的更新增量来更新潜在因素，而没有适当考虑过去的学习信息，从而导致收敛缓慢。受比例综合（PI）控制器在各种应用中取得的突出成功的启发，本文提出了一个成比例的成比例成分的非负性潜伏因素（PI-NLF）模型，具有两个倍数的想法： b）设计基于IR的SLF-NMU（ISN）算法以加速所得模型的收敛速率。对四个HDI数据集的实证研究表明，PI-NLF模型在计算效率和估计精度中的最先进模型对于缺少HDI矩阵的数据的模型。因此，这项研究揭示了通过错误反馈控制器提高非负学习算法的性能的可行性。
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes. A non-negative latent factor (NLF) model performs efficient representation learning to an HDI matrix, whose learning process mostly relies on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm updates a latent factor based on the current update increment only without appropriate considerations of past learning information, resulting in slow convergence. Inspired by the prominent success of a proportional-integral (PI) controller in various applications, this paper proposes a Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model with two-fold ideas: a) establishing an Increment Refinement (IR) mechanism via considering the past update increments following the principle of a PI controller; and b) designing an IR-based SLF-NMU (ISN) algorithm to accelerate the convergence rate of a resultant model. Empirical studies on four HDI datasets demonstrate that a PI-NLF model outperforms the state-of-the-art models in both computational efficiency and estimation accuracy for missing data of an HDI matrix. Hence, this study unveils the feasibility of boosting the performance of a non-negative learning algorithm through an error feedback controller.
