Which principal components to utilize for principal component regression
Which principal components to utilize for principal component regression
复制标题
主成分回归使用哪些主成分
DOI：
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发表时间：
1992
期刊：
影响因子：
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通讯作者：
P. Lang
中科院分区：
文献类型：
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作者：
J. Sutter;J. Kalivas;P. Lang
主成分回归 (PCR) 的主成分 (PC) 历来都是从上到下选择的，以获得可靠的预测模型。也就是说，PC被排列在从信息量最大的（与最大奇异值相关联的PC）开始并继续到信息量最少（与最小奇异值相关联的PC）的列表中。然后从该列表的顶部开始选择 PC。本文讨论了将 PC 选择视为优化问题的替代过程。具体来说，在不考虑排序的情况下，需要可接受的预测模型的最佳 PC 子集。使用传统方法和替代方法分析五个数据集。两个数据集本质上是光谱数据，两个数据集处理定量构效关系（QSAR），一个数据集涉及建模。所有五个数据集均证实，在不考虑顺序的情况下选择子集可确保 PCR 获得最佳结果。还使用偏最小二乘法 1 比较一组数据。
Principal components (PCs) for principal component regression (PCR) have historically been selected from the top down for a reliable predictive model. That is, the PCs are arranged in a list starting with the most informative (PC associated with the largest singular value) and proceeding to the least informative (PC associated with the smallest singular value). PCs are then chosen starting at the top of this list. This paper discusses an alternative procedure of treating PC selection as an optimization problem. Specifically, without any regard to the ordering, the optimal subset of PCs for an acceptable predictive model is desired. Five data sets are analyzed using the conventional and alternative approaches. Two data sets are spectroscopic in nature, two data sets deal with quantitative structure‐activity relationships (QSARs) and one data set is concerned with modeling. All five data sets confirm that selection of a subset without consideration to order secures the best results with PCR. One data set is also compared using partial least squares 1.