Random Matrix Methods for Machine Learning

Random Matrix Methods for Machine Learning
This is a PDF. Digital Download, No physical item will be shipped.

This book presents a comprehensive theory of random matrices applied to machine learning, focusing on high-dimensional data analysis leveraging concentration and universality phenomena. It provides insights into the underlying mechanisms of real-world machine learning algorithms, aiming to enhance their effectiveness. Beginning with a solid theoretical foundation of random matrices, the book supports a broad range of applications including SVMs, semi-supervised learning, unsupervised spectral clustering, graph methods, neural networks, and deep learning. Each application is explored with insights into both small- and large-dimensional scenarios, followed by a systematic analysis using random matrix theory to evaluate performance and suggest enhancements. The concepts, applications, and variations are demonstrated through numerical examples using synthetic and real-world data, complemented by MATLAB and Python code available on the book's website.