论文标题
使用稳定的矢量贪婪核法的生物力学替代建模
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
论文作者
论文摘要
贪婪的内核近似算法是成功的技术，用于稀疏和准确的基于数据的建模和功能近似。基于在标量输出案例中这种算法稳定的最新想法，我们在这里考虑构建基于Vkoga的矢量扩展。我们介绍了所谓的$γ$限制性vkoga，对分析性能的评论，并对来自临床相关应用的数据（人脊柱建模）进行数值评估。实验表明，新的稳定算法可提高非稳定算法的准确性和稳定性。
Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation. Based on a recent idea of stabilization of such algorithms in the scalar output case, we here consider the vectorial extension built on VKOGA. We introduce the so called $γ$-restricted VKOGA, comment on analytical properties and present numerical evaluation on data from a clinically relevant application, the modelling of the human spine. The experiments show that the new stabilized algorithms result in improved accuracy and stability over the non-stabilized algorithms.
