TY  - BOOK
AU  - Couillet,Romain
AU  - Liao,Zhenyu
TI  - Random matrix methods for machine learning
SN  - 9781009123235
AV  - Q325.5 .C69 2023
U1  - 006.310151922 23
PY  - 2023///
CY  - Cambridge, UK, New York, NY
PB  - Cambridge University Press
KW  - Machine learning
KW  - Mathematics
KW  - Matrix analytic methods
N1  - Includes bibliographical references and index; Introduction -- Random matrix theory -- Statistical inference in linear models -- Kernel methods -- Large neural networks -- Large-dimensional convex optimization -- Community detection on graphs -- Universality and real data
N2  - "Numerous and large dimensional data is now a default setting in modern machine learning (ML). Standard ML algorithms, starting with kernel methods such as support vector machines and graph-based methods like the PageRank algorithm, were however initially designed out of small dimensional intuitions and tend to misbehave, if not completely collapse, when dealing with real-world large datasets. Random matrix theory has recently developed a broad spectrum of tools to help understand this new curse of dimensionality, to help repair or completely recreate the sub-optimal algorithms, and most importantly to provide new intuitions to deal with modern data mining"--
ER  -