论文标题
高维和稀疏数据的差分进化增强的潜在因子分析模型
A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data
论文作者
论文摘要
经常采用高维和稀疏（HID）矩阵来描述各种大数据相关的系统和应用中的复杂关系。位置转变潜在因子分析（PLFA）模型可以准确有效地表示HID矩阵。但是，其所涉及的潜在因素通过随机梯度下降和特定梯度方向逐步优化，这可能会导致次优溶液。为了解决这个问题，本文提出了一种顺序分别分化的进化（SGDE）算法，以优化通过PLFA模型优化的潜在因素，从而实现了高度精确的SGDE-PLFA模型来隐藏矩阵。正如实验在四个HID矩阵上所证明的那样，SGDE-PLFA模型的表现优于最新模型。
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a Sequential-Group-Differential- Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model outperforms the state-of-the-art models.
