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：本文研究多元函数数据的分类（监督学习）问题，其中随机函数向量的各分量定义于不同域上。在此框架下，我们提出了适用于多元函数数据的偏最小二乘分类方法及基于树结构的偏最小二乘改进方法。从计算角度出发，我们证明了多元函数数据回归中的偏最小二乘成分可通过单变量函数数据的偏最小二乘方法直接获得。这为多元函数数据的偏最小二乘算法提供了一种新的实现途径。