High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix.

翻译：高维非完整（HDI）矩阵包含众多节点间的复杂交互。基于随机梯度下降（SGD）的潜在因子分析（LFA）模型在从HDI矩阵中提取有效信息方面效果显著。然而，此类模型常面临收敛缓慢的问题，因为标准SGD算法仅依据当前学习误差计算随机梯度，而未考虑学习误差的历史和未来状态。为解决这一关键问题，本文创新性地提出一种融合自抗扰控制的SGD（ADS）算法，通过遵循自抗扰控制器原理，综合考虑历史与未来状态来优化实例学习误差。基于此，进一步实现了基于ADS的LFA模型，用于对HDI矩阵进行快速且准确的潜在因子分析。在两个HDI数据集上的实证研究表明，所提模型在预测HDI矩阵缺失数据的计算效率与准确性方面均优于当前最先进的LFA模型。