融合差分进化与梯度下降，提升高维稀疏数据的表征质量
An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization
- 用差分进化和梯度下降分别训练两个不同因子模型
- 在三个真实数据集上表现优于现有因子模型
- 适合处理异质性高维稀疏数据的场景
高维稀疏（HDI）数据广泛存在于各类大数据场景中。潜因子模型作为常用表示学习方法，可从中挖掘有意义的潜在特征。然而，多数现有模型仅依赖梯度下降优化，易产生不充分且有偏差的表征，尤其在异质性较强的HDI数据上。为此，本文提出一种基于差分进化与梯度下降联合优化的集成潜因子模型（ELFM-DEGDO），其设计包含两方面：1）分别采用差分进化和梯度下降独立建模两个差异化潜因子模型；2）通过自适应加权机制融合二者，有效整合优势。该方法利用两种优化范式的互补性，生成更全面、偏差更小的表征。在三个真实HDI数据集上的实验表明，ELFM-DEGDO始终优于多个对比潜因子模型。
原文摘要 · Abstract (English)
High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios. Latent factor models serve as a common representation learning approach, capable of uncovering informative latent factors from such data. Nevertheless, most existing latent factor models rely solely on gradient descent for optimization, which may lead to insufficient and biased representations, particularly when dealing with heterogeneous HDI data. Thus, this study proposes an Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization (ELFM-DEGDO) with two-fold designed: 1) two diverse latent factor models are independently modeled via differential evolution and gradient descent optimization, respectively, and 2) the two diverse latent factor models are combined via a customized self-adaptive weighting mechanism to effectively fuse their strengths. By leveraging the complementary advantages of both optimization paradigms, ELFM-DEGDO is able to produce more comprehensive and less biased representations for HDI data. Three HDI datasets are tested to show that ELFM-DEGDO consistently performs better than related several latent factor models.
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