Adaptive Cubic Regularized Second-Order Latent Factor Analysis Model

• URL: http://arxiv.org/abs/2507.03036v1
• Date: Thu, 03 Jul 2025 03:15:54 GMT
• Title: Adaptive Cubic Regularized Second-Order Latent Factor Analysis Model
• Authors: Jialiang Wang, Junzhou Wang, Xin Liao,
• Abstract summary: High-dimensional and incompleteHDI datasets have become ubiquitous across various real-world applications.<n>We propose a two-fold approach to mitigate information instabilities.<n>The ACRS HDI demonstrate that the ALF represents higher representation than the faster advancing (SACR) models.
• Score: 14.755426957558868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-dimensional and incomplete (HDI) data, characterized by massive node interactions, have become ubiquitous across various real-world applications. Second-order latent factor models have shown promising performance in modeling this type of data. Nevertheless, due to the bilinear and non-convex nature of the SLF model's objective function, incorporating a damping term into the Hessian approximation and carefully tuning associated parameters become essential. To overcome these challenges, we propose a new approach in this study, named the adaptive cubic regularized second-order latent factor analysis (ACRSLF) model. The proposed ACRSLF adopts the two-fold ideas: 1) self-tuning cubic regularization that dynamically mitigates non-convex optimization instabilities; 2) multi-Hessian-vector product evaluation during conjugate gradient iterations for precise second-order information assimilation. Comprehensive experiments on two industrial HDI datasets demonstrate that the ACRSLF converges faster and achieves higher representation accuracy than the advancing optimizer-based LFA models.

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