Random Matrix Methods for Machine Learning

ISBN-13: 9781009123235 / Angielski / Twarda / 2022 / 450 str.

Zhenyu Liao

Opis

This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.

Recenzje

'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

Spis treści

Preface; 1. Introduction; 2. Random matrix theory; 3. Statistical inference in Linear Models; 4. Kernel methods; 5. Large neural networks; 6. Large dimensional convex optimization; 7. Community detection on graphs; 8. Universality and real data; Bibliography; Index.