  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.
