Yan Muller, Department of Computer Science, University of Kaiserslautern-Landau (RPTU)

Title: Partial Least Squares Regression

Abstract:

This thesis investigates the use of Partial Least Squares (PLS) regression for dimensionality
reduction in machine learning applications. A simple linear experiment first
demonstrates the basic functionality of PLS. Subsequently, Linear Regression (LR) and
Gaussian Process (GP) models are evaluated on a strictly nonlinear target function under
three scenarios: without dimensionality reduction, with PLS, and with Principal
Component Analysis (PCA). The experiments, conducted on datasets with low to moderate
dimensionality, show that dimensionality reduction through PLS and PCA does not
improve runtime or predictive performance in these settings. Gaussian Process models
without dimensionality reduction achieved the best results. While dimensionality reduction
can, in principle, approximate the predictive performance of a full-dimensional GP
model with fewer input variables, our findings emphasize that its success strongly depends
on the dataset properties and the number of components selected.