Random matrix methods for machine learning /
Couillet, Romain, 1983-
Random matrix methods for machine learning / Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology. - 1 online resource (vi, 402 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 30 Jun 2022).
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.
9781009128490 (ebook)
Machine learning--Mathematics.
Matrix analytic methods.
Q325.5 / .C69 2022
006.31
Random matrix methods for machine learning / Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology. - 1 online resource (vi, 402 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 30 Jun 2022).
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.
9781009128490 (ebook)
Machine learning--Mathematics.
Matrix analytic methods.
Q325.5 / .C69 2022
006.31
