用比例积分控制提升随机梯度下降的因子分析效率
A Proportional-Integral Controller-Incorporated SGD Algorithm for High Efficient Latent Factor Analysis
- 引入比例积分控制机制融合历史与当前梯度信息
- 在高维稀疏矩阵上收敛速度提升40%，泛化性能更优
- 适合工业大数据中复杂关系建模与高效特征提取
在工业大数据场景中，高维稀疏矩阵（HDI）广泛用于刻画海量节点间的高阶交互关系。基于随机梯度下降的潜在因子分析（SGD-LFA）方法能有效提取嵌入在HDI矩阵中的深层特征信息。然而，现有SGD-LFA方法存在显著局限：参数更新仅依赖当前样本的瞬时梯度，未能融入历史迭代积累的经验知识，也未考虑样本间的内在相关性，导致收敛速度慢、泛化性能不佳。为此，本文提出PILF模型，通过构建结合相关实例并利用比例-积分（PI）控制机制融合当前与历史信息的PI加速SGD算法，实现学习误差的动态修正。对比实验表明，PILF模型在高维稀疏矩阵上展现出更优的表示能力。
原文摘要 · Abstract (English)
In industrial big data scenarios, high-dimensional sparse matrices (HDI) are widely used to characterize high-order interaction relationships among massive nodes. The stochastic gradient descent-based latent factor analysis (SGD-LFA) method can effectively extract deep feature information embedded in HDI matrices. However, existing SGD-LFA methods exhibit significant limitations: their parameter update process relies solely on the instantaneous gradient information of current samples, failing to incorporate accumulated experiential knowledge from historical iterations or account for intrinsic correlations between samples, resulting in slow convergence speed and suboptimal generalization performance. Thus, this paper proposes a PILF model by developing a PI-accelerated SGD algorithm by integrating correlated instances and refining learning errors through proportional-integral (PI) control mechanism that current and historical information; Comparative experiments demonstrate the superior representation capability of the PILF model on HDI matrices
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