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

arXiv: https://arxiv.org/abs/2212.09145 · HTML: https://mlsanity.com/paper/2212.09145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [Multivariate Planar Curves: A Statistical Framework for Shape Analysis in Images](https://mlsanity.com/paper/2508.11780/) (2025)
- [Enriched Functional Tree-Based Classifiers: A Novel Approach Leveraging Derivatives and Geometric Features](https://mlsanity.com/paper/2409.17804/) (2024)

## Abstract (arXiv)

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.