Computer Science > Machine Learning
[Submitted on 2 Apr 2022 (v1), last revised 16 Apr 2022 (this version, v2)]
Title:A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data
View PDFAbstract: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.
Submission history
From: Jia Chen [view email][v1] Sat, 2 Apr 2022 13:41:19 UTC (464 KB)
[v2] Sat, 16 Apr 2022 15:28:07 UTC (474 KB)
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