Classification of multivariate functional data on different domains with Partial Least Squares approaches

TL;DR

This paper introduces PLS-based classification methods for multivariate functional data defined on different domains, providing computationally efficient algorithms that leverage univariate PLS techniques.

Contribution

It presents novel PLS classification and tree PLS methods tailored for multivariate functional data across different domains, simplifying computation.

Findings

PLS components can be derived using univariate functional data methods.

Proposed algorithms are computationally efficient.

Methods are applicable to multivariate data on different domains.

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.