Leveraging Shared Factor Structures for Enhanced Matrix Completion with Nonconvex Penalty Regularization
利用共享因子结构与非凸惩罚改进矩阵补全
This article investigates the problem of noisy low-rank matrix completion with a shared factor structure, leveraging the auxiliary information from the missing indicator matrix to enhance prediction accuracy. Despite decades of development in matrix completion, the potential relationship between observed data and missing indicators has largely been overlooked. To address this gap, we propose a joint modeling framework for the observed data and missing indicators within the…