PLS pruning: a new approach to variable selection for multivariate calibration based on Hessian matrix of errors
PLS pruning: a new approach to variable selection for multivariate calibration based on Hessian matrix of errors
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DOI：
10.1016/j.chemolab.2004.09.007
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发表时间：
2005-03-28
影响因子：
3.9
通讯作者：
Poppi, RJ
中科院分区：
文献类型：
--
作者：
Lima, SLT;Mello, C;Poppi, RJ
针对偏最小二乘模型中的变量选择问题，提出了一种新的偏最小二乘剪枝方法。该方法的目的是通过使用来自误差函数的所有二阶导数的信息来删除不重要的偏最小二乘回归系数。将该方法用于近红外光谱分析甘蔗汁中糖度。所获得的结果是有希望的，导致有意义的变量减少96%，而不损失模型预测能力。(C)2004爱思唯尔B.V.保留所有权利。
In this article, a new approach called partial least squares (PLS) pruning is described for variable selection in PLS modeling. The aim of the method is the deletion of unimportant PLS coefficients of regression by using information from all second derivatives of the error function. The proposed approach was applied to Brix determination in sugar cane juice by near infrared spectroscopy. The results obtained were promising, leading to a meaningful variable reduction of 96% without loss of model prediction capability. (c) 2004 Elsevier B.V. All rights reserved.