Elimination of uninformative variables for multivariate calibration
Elimination of uninformative variables for multivariate calibration
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DOI：
10.1021/ac960321m
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发表时间：
1996-11-01
影响因子：
7.4
通讯作者：
Sterna, C
中科院分区：
文献类型：
--
作者：
Centner, V;Massart, DL;Sterna, C
提出了一种剔除多元数据中无信息变量的新方法。为了实现这一点，添加人工（噪声）变量，并获得包含实验和人工变量的数据集的PLS或PCR模型的封闭形式。从基于B系数的标准判断，不具有比人工变量更重要的实验变量被消除。在模拟数据上对该方法的性能进行了评估，在实验获得的近红外数据集上讨论了实用方面，得出的结论是，消除无信息变量可以提高预测能力。
A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a criterion based on the b coefficients, are eliminated. The performance of the method is evaluated on simulated data, Practical aspects are discussed on experimentally obtained near-IR data sets, It is concluded that the elimination of uninformative variables can improve predictive ability.