Partial Least Square

Partial Least Squares (PLS) is a multivariate statistical technique used to model the relationship between a set of predictor variables and one or more response variables, particularly when dealing with high-dimensionality and collinearity. Current research focuses on enhancing PLS's robustness and efficiency through adaptations like incorporating weighted least squares, applying it within federated learning frameworks for privacy-preserving data analysis, and optimizing its performance with kernel methods to handle non-linear relationships. These advancements are improving PLS's applicability in diverse fields, including chemometrics, biotechnology, and imaging genetics, enabling more accurate predictions and insightful data analysis in complex datasets.