基于稳定向量贪婪核方法的生物力学代理建模
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
摘要：
贪婪核近似算法是稀疏和精确的基于数据的建模和函数近似的成功技术。基于最近的想法，稳定的标量输出的情况下，这样的算法，我们在这里考虑的矢量扩展VKOGA。我们介绍了所谓的$γ$限制VKOGA，评论分析性能和目前的数值评估数据从临床相关的应用，人体脊柱的建模。实验结果表明，新的稳定算法的结果提高了准确性和稳定性比非稳定算法。
英文摘要：
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.
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