SALSA – a spatially adaptive local smoothing algorithm
SALSA – a spatially adaptive local smoothing algorithm
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SALSA——空间自适应局部平滑算法
DOI：
10.1080/00949650903229041
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发表时间：
2011
影响因子：
1.2
通讯作者：
Michael O'Sullivan
中科院分区：
文献类型：
--
作者：
C. Walker;Monique MacKenzie;Carl Donovan;Michael O'Sullivan
我们提出了一个非线性整数规划公式，用于使用自适应结选择方法拟合基于样条的二维数据回归，并在求解过程中确定结的数量和位置。然而，该公式的非线性性质使其解不切实际，因此我们还概述了受Remes交换算法启发的结选择启发式，以产生我们公式的良好解。该算法直观，能够很自然地适应局部平滑度的变化。结果表明，在已建立的基准函数上，该算法的性能与其他当前方法一样好，甚至更好。
We present a nonlinear integer programming formulation for fitting a spline-based regression to two-dimensional data using an adaptive knot-selection approach, with the number and location of the knots being determined in the solution process. However, the nonlinear nature of this formulation makes its solution impractical, so we also outline a knot selection heuristic inspired by the Remes Exchange Algorithm, to produce good solutions to our formulation. This algorithm is intuitive and naturally accommodates local changes in smoothness. Results are presented for the algorithm demonstrating performance that is as good as, or better than, other current methods on established benchmark functions.