Event Information:

• Thu

  22

  May

  2025

  SC Seminar: Yan Muller

  9:30Room 32-349

  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.