Supervised Dimensionality Reduction

Supervised dimensionality reduction aims to reduce the number of variables in a dataset while preserving information relevant to a target variable, improving data visualization and model performance. Current research focuses on enhancing existing methods like principal component analysis (PCA) with supervised constraints, developing novel algorithms based on concepts like random forests, autoencoders, and even physics-inspired approaches (e.g., gravitational models), and addressing challenges such as out-of-sample extension and handling incomplete data. These advancements are significant for improving the interpretability and efficiency of machine learning models across diverse fields, from vehicle dynamics estimation to medical image analysis and beyond.