Random Matrix Applications in Machine Learning

Random matrix theory involves studying matrices filled with random numbers and analyzing their properties. In machine learning, it helps understand complex data patterns, improve algorithms' robustness, and optimize training processes. For example, it can evaluate the stability of neural networks, analyze the behavior of large datasets, and enhance techniques like principal component analysis. By providing insights into the structure and behavior of high-dimensional data, random matrix applications help create more reliable, efficient, and scalable machine learning models, ultimately leading to better performance in real-world applications.