Random Matrix Methods for Machine Learning (eBook)

Romain Couillet, Zhenyu Liao (Autoren)

eBook Download: PDF

2022
Cambridge University Press (Verlag)
978-1-009-30189-3 (ISBN)

Infos

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.

Produkt-Details

• Erscheint lt. Verlag: 28.7.2022
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik
• ISBN-10: 1-009-30189-6 / 1009301896
• ISBN-13: 978-1-009-30189-3 / 9781009301893