Partial Least Squares: Bridging the Gap in Multivariate Analysis

<h2>The Genesis and Evolution of PLS</h2> <p>Conceived in the late 20th century, PLS was initially developed as a response to the limitations encountered in traditional statistical methods when dealing with highly collinear data or scenarios where the predictors outnumber the observations. The method, through its ingenious approach to dimensionality reduction and latent variable modeling, has since transcended its initial applications, finding utility across a broad spectrum of disciplines including chemometrics, sensory analysis, finance, and social sciences.</p> <h2>The Methodological Core of PLS</h2> <p>At its core, PLS seeks to model the relationship between a set of predictor variables (X) and one or more response variables (Y) by extracting latent variables. These latent variables are linear combinations of the original predictors designed to capture the maximum relevant information necessary for predicting the response. This process not only simplifies the complexity inherent in high-dimensional data but also enhances the predictive accuracy of the model by focusing on the most informative aspects of the predictors.</p> <p><a href="https://pub.aimind.so/partial-least-squares-bridging-the-gap-in-multivariate-analysis-5376df6baaa0">Website</a></p> <p>&nbsp;</p>