多任务Kriging变量选择的研究与应用
Research and Application on Variable Selection in Multi-Task Kriging Model
摘要: 本文研究多任务Kriging模型的变量选择问题，并给出多种稀疏化惩罚下多任务Kriging的变量选择算法。数值模拟及实例分析表明，相比单任务的Kriging变量选择，多任务模式能显著提高计算效率而不失模型拟合的准确性；相比LMC及卷积模型，多任务稀疏化Kriging能有效提取任务间的共性信息，极大节约计算成本同时提高预测精度。
Abstract: We study the variable selection in multi-task Kriging model and develop the algorithms for com-monly used penalizations. In numerical simulations, our multi-task penalized approach achieves higher computational efficiency without loss of accuracy and stability compared to the single-task approach. In real data application, multi-task penalized Kriging effectively captures shared features among tasks and thus reduces computational burden compared with the LMC and CONV models.
文章引用：纪洁, 邹晨晨. 多任务Kriging变量选择的研究与应用[J]. 应用数学进展, 2023, 12(3): 1224-1230. https://doi.org/10.12677/AAM.2023.123124

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