Random Matrix Methods for Machine Learning
- Length: 408 pages
- Edition: 1
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2022-10-31
- ISBN-10: 1009123238
- ISBN-13: 9781009123235
- Sales Rank: #5275905 (See Top 100 Books)
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.
0.0 9781009123235 01.0_pp_i_iv_Frontmatter 02.0_pp_v_vi_Contents 03.0_pp_vii_viii_Preface 04.0_pp_1_34_Introduction 05.0_pp_35_154_Random_Matrix_Theory 06.0_pp_155_206_Statistical_Inference_in_Linear_Models 07.0_pp_207_276_Kernel_Methods 08.0_pp_277_312_Large_Neural_Networks 09.0_pp_313_336_Large-Dimensional_Convex_Optimization 10.0_pp_337_363_Community_Detection_on_Graphs 11.0_pp_364_377_Community_Detection_on_Graphs 12.0_pp_378_400_Bibliography 13.0_pp_401_402_Index
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