MATLAB-based Partial Least Squares Regression Implementation

In this article, we present an effective MATLAB-based implementation of Partial Least Squares Regression (PLSR). This program serves as a powerful tool for data analysis and modeling applications. Before utilizing this program, users should understand the fundamental concepts and applications of PLSR to maximize its potential. PLSR is a supervised statistical method designed to analyze relationships between two or more datasets and predict unknown outcomes. The implementation handles both linear and nonlinear relationships between variables, making it particularly versatile for various domains including finance, medical research, and engineering applications. The MATLAB implementation typically includes key functions such as: - plsregress(): Core function for performing PLSR computation - Cross-validation routines for optimal component selection - Visualization tools for loading plots and score plots - Prediction functions for new data validation The algorithm follows these key steps: 1. Preprocessing: Data normalization and scaling 2. Component extraction: Iterative calculation of latent variables 3. Regression coefficient computation: Building the predictive model 4. Model validation: Using metrics like RMSE and Q² for performance evaluation This implementation efficiently handles high-dimensional data and multicollinearity issues through dimensionality reduction techniques, while maintaining predictive accuracy through proper component selection.