Random Matrix Methods for Machine Learning
Romain Couillet author Zhenyu Liao author
Format:Hardback
Publisher:Cambridge University Press
Published:21st Jul '22
Should be back in stock very soon
This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.
For graduate students, practitioners, and sophisticated users, this book offers a tutorial approach to the foundations of random matrix theory for machine learning and systematic analyses of advanced applications ranging from power detection to deep neural networks. MATLAB and Python code is provided for all concepts and applications.This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.
'Roman Couillet's book is unique among books on random matrix theory in that it provides a solid yet accessible introduction to the theory and its transformative potential in applications. After presenting a coherent and uncluttered introduction of the theory, several chapters illustrate how it applies to important problems, including, high dimensional statistical inference, neural networks, random graphs, and convex optimization. Written in a self-contained and exceptionally clear style this book will be of great utility to researchers in machine learning, statistics and signal processing who want to learn about how random matrix theory can be applied.' Alfred Hero, University of Michigan
'This book is a reference for all engineers and researchers interested in the recent mathematical advances in Machine Learning. It's a real 'tour de force' that fruitfully combines the mathematical elegance of random matrix theory methods with an impressive range of real-world applications.' Merouane Debbah, Huawei France Research Center
'This is a very timely and important book. Romain Couillet and Zhenyu Liao provide a great entry point into active, recent research on the applications of Random Matrix Theory as it pertains to high-dimensional statistics and analysis of machine learning algorithms. RMT was born in statistics with Wishart and later became, via Wigner, a great pillar of quantum and statistical before being recently pushed by mathematicians to deeper universality results. It is quite fitting that it now comes back to the modern problems and methods of statistics with this very well-organized and carefully written book by two leading experts.' Gérard Ben Arous, Courant Institute of Mathematical Sciences, New York University
ISBN: 9781009123235
Dimensions: 251mm x 174mm x 23mm
Weight: 870g
408 pages