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：本文考虑当随机函数向量中各元素定义在不同域上时，对多元函数型数据进行分类（监督学习）的问题。针对该场景，提出了多元函数型数据的偏最小二乘（PLS）分类及基于树形PLS的方法。从计算视角出发，我们证明多元函数型数据回归的PLS分量可仅通过单变量函数型数据的PLS方法获得。这为多元函数型数据的PLS算法提供了一种新的实现思路。