Detecting influential observations by cluster analysis and Monte Carlo cross-validation.
Detecting influential observations by cluster analysis and Monte Carlo cross-validation.
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DOI：
10.1039/c0an00345j
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
X. Bian;W. Cai;X. Shao;Da Chen;E. Grant
中科院分区：
文献类型：
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作者：
X. Bian;W. Cai;X. Shao;Da Chen;E. Grant
有影响观测值的检测是建立高性能模型的重要步骤，在许多工业和实验室应用中被认为是一项重要而具有挑战性的任务。提出了一种基于影响观测值对偏最小二乘（PLS）建模的影响来检测影响观测值的方法。在该方法中，我们通过使用蒙特卡罗交叉验证（MCCV）建立大量的PLS模型，然后对这些模型的回归系数进行主成分分析（PCA）。由于具有影响性观测值的模型与不具有影响性观测值的模型不同，因此基于它们所包含的不同数量的影响性观测值，PLS模型系列在主成分（PC）空间中聚类成不同的组。因此，有影响的意见，可以识别根据每个样本在每个组中的频率数。通过对近红外和拉曼光谱的三个实例进行定量建模，表明该方法可以直观、准确地检测出有影响的观测值。
The detection of influential observations is an essential step for building high performance models and has been recognized as an important and challenging task in many industrial and laboratorial applications. A new approach for detecting influential observations is developed based on their effect on partial least squares (PLS) modeling. In this method, we build a large number of PLS models by using Monte Carlo cross-validation (MCCV), and then perform principal component analysis (PCA) on the regression coefficients of these models. Because a model with influential observations is different from the one without influential observation, the series of PLS models cluster into different groups in principal component (PC) spaces, based on the different number of influential observations they contain. The influential observations can be therefore recognized according to the frequency number of each sample in each group. By three examples quantitatively modeling near-infrared (NIR) and Raman spectra, it was shown that the method can detect the influential observations intuitively and veraciously.