Classification of multivariate functional data on different domains with Partial Least Squares approaches
Methodology
2024-06-11 v3 Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2212.09145,
title = {Classification of multivariate functional data on different domains with Partial Least Squares approaches},
author = {Issam-Ali Moindjie and Sophie Dabo-Niang and Cristian Preda},
journal= {arXiv preprint arXiv:2212.09145},
year = {2024}
}
Comments
enhance readability, new simulation setting, correction of minor mathematical notations errors, rewrite the conclusion