RMT in Machine Learning

Random Matrix Theory (RMT) in machine learning studies the behavior of large data sets, particularly in understanding patterns and structures within them. It uses mathematical concepts from statistics and physics to analyze how data dimensions interact. RMT helps researchers identify relevant features, improve algorithm performance, and address challenges like overfitting. By modeling data as matrices, RMT provides insights into complex relationships and enhances network training, making it a valuable tool for analyzing high-dimensional data scenarios in various applications, such as finance, neuroscience, and image processing.