Random Matrix Methods for Machine Learning
Author: Romain Couillet and Zhenyu Liao
Publisher finelybook 出版社: Cambridge University Press; 1st edition (October 31, 2022)
Language 语言: English
Print Length 页数: 408 pages
ISBN-10: 1009123238
ISBN-13: 9781009123235
Book Description
By finelybook
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 Author: 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.
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Random Matrix Methods for Machine Learning 9781009123235.rar
