TY  - BOOK
AU  - Couillet,Romain
AU  - Liao,Zhenyu
TI  - Random matrix methods for machine learning
SN  - 9781009128490 (ebook)
AV  - Q325.5 .C69 2022
U1  - 006.31 23
PY  - 2022///
CY  - Cambridge, United Kingdom ; New York, NY, USA
PB  - Cambridge University Press
KW  - Machine learning
KW  - Mathematics
KW  - Matrix analytic methods
N1  - Title from publisher's bibliographic system (viewed on 30 Jun 2022)
N2  - 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
UR  - https://doi.org/10.1017/9781009128490
ER  -