High-Dimensional Data Analysis with
Low-Dimensional Models:
Principles, Computation, and Applications
Basic Information
The book covers new mathematical principles (statistics
and geometry) for high-dimensional data analysis, scalable (convex and
nonconvex) optimization methods, and important applications such as
scientific imaging, wideband communications, face recognition, 3D
vision, and deep networks. This book is to be used as an introductory graduate textbook for the
areas of data science, signal processing, optimization, and machine
learning. It has been used for the courses EECS 290 (Berkeley)
and ELEN 6886 (Columbia).
Download and Copyright
Download a pre-production copy: Book-Wright-Ma.pdf (full book coming soon). Copyright of this book is
held by Cambridge University Press, who have kindly allowed us to post a
pre-publication version on this website.
Copyright statement:
This material will be published by Cambridge University Press as
"High-Dimensional Data Analysis with Low-Dimensional Models:
Principles, Computation, and Applications" by John Wright
and Yi Ma. This pre-publication version is free to view and download for
personal use only, and is not for redistribution, re-sale or use in
derivative works. Copyright © Cambridge
University Press 2018.
Catalog Links
Hardcopy of the book is available at: TBA
Errata and Resources
Reader feedback: If you have any feedback, suggestions, or
errors to report, please send us an email to: book.wright.ma@gmail.com
Errata: TBA
Additional resources: TBA
©2020 Yi Ma
Last modified: Sat Nov 21 15:40:08 PST 2020