With the development of technology and the relatively higher availability of new instrumentations, having multiblock data sets (eg, a set of samples analyzed by different analytical techniques) is becoming more and more common and, as a consequence, how to handle this kind of outcomes is a widely discussed topic. In such a context, where the number of involved variables is relatively high, selecting the most significant features is obviously relevant. For this reason, the possibility of joining a multiblock regression method, the sequential and orthogonalized partial least‐squares (SO‐PLS), with a variable selection approach called covariance selection (CovSel), has been investigated. The resulting method, sequential and orthogonalized covariance selection (SO‐CovSel) is similar to SO‐PLS, but the feature reduction provided by PLS is performed by CovSel. Finally, predictions are made by applying multiple linear regression on the subset of selected variables. The novel approach has been tested on different multiblock data sets both in regression and in classification (by combination with LDA), and it has been compared with another state‐of‐the‐art multiblock method. SO‐CovSel has demonstrated to be suitable for its purpose: It has provided good predictions (both in regression and in classification) and, from the interpretation point of view, it has led to a meaningful selection of the original variables.

中文翻译：

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SO-CovSel：一种多块框架中变量选择的新方法

随着技术的发展和新仪器相对较高的可用性，拥有多块数据集（例如，通过不同分析技术分析的一组样本）变得越来越普遍，因此，如何处理这种结果是一个广泛讨论的话题。在这种情况下，涉及的变量数量相对较多，选择最重要的特征显然是相关的。出于这个原因，已经研究了将多块回归方法、顺序和正交偏最小二乘法 (SO-PLS) 与称为协方差选择 (CovSel) 的变量选择方法结合使用的可能性。由此产生的方法，顺序和正交协方差选择（SO-CovSel）类似于 SO-PLS，但是 PLS 提供的特征减少是由 CovSel 执行的。最后，通过对所选变量的子集应用多元线性回归来进行预测。这种新方法已经在回归和分类（通过与 LDA 结合）的不同多块数据集上进行了测试，并与另一种最先进的多块方法进行了比较。SO-CovSel 已证明适合其目的：它提供了良好的预测（在回归和分类中），并且从解释的角度来看，它导致了对原始变量的有意义的选择。这种新方法已经在回归和分类（通过与 LDA 结合）的不同多块数据集上进行了测试，并与另一种最先进的多块方法进行了比较。SO-CovSel 已证明适合其目的：它提供了良好的预测（在回归和分类中），并且从解释的角度来看，它导致了对原始变量的有意义的选择。这种新方法已经在回归和分类（通过与 LDA 结合）的不同多块数据集上进行了测试，并与另一种最先进的多块方法进行了比较。SO-CovSel 已证明适合其目的：它提供了良好的预测（在回归和分类中），并且从解释的角度来看，它导致了对原始变量的有意义的选择。