Classification (supervised-learning) of multivariate functional data is considered when the elements of the random functional vector of interest are defined on different domains. In this setting, PLS classification and tree PLS-based methods for multivariate functional data are presented. From a computational point of view, we show that the PLS components of the regression with multivariate functional data can be obtained using only the PLS methodology with univariate functional data. This offers an alternative way to present the PLS algorithm for multivariate functional data.

翻译：本文考虑多维函数数据的分类（监督学习），当感兴趣的随机函数向量的元素在不同的域上定义时。在这个设置中，本文介绍了多维函数数据的偏最小二乘分类和基于树的偏最小二乘方法。从计算的角度，我们展示了通过只使用单变量函数数据的偏最小二乘方法结合多维函数数据可以获得回归的偏最小二乘成分。这为多维函数数据的偏最小二乘算法提供了一种可选方法。