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High-Dimensional Data Analysis with
Low-Dimensional Models:
Principles, Computation, and Applications
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Basic Information
The book covers new mathematical (statistical, geometrical, computational) principles for high-dimensional data analysis, with
scalable optimization methods and their applications in important real-world problems such as
scientific imaging, wideband communications, face recognition, 3D
vision, and deep networks. Comprehensive in its approach, the book provides unified
coverage of many different low-dimensional models and analytical techniques,
including sparse, low-rank, and deep network models, with both convex and nonconvex formulations.
This textbbook is intended for an introductatory graduate course that helps students establish a solid foundation for the
areas of data science, signal processing, optimization, and machine
learning. Early versions of this book have been used as the textbook for courses at University of Illinois, University of Californina at Berkeley, Columbia University,
Tsinghua University, ShanghaiTech University, and University of Michigan etc.
Download a Preproduction Version
Copyright statement:
A complete version of the manuscript was first released here online on December 06, 2020. The latest version reflects small changes made to the original.
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.
Citation (bibtex):
@book{Wright-Ma-2022,
author = {John Wright and Yi Ma},
title = {High-Dimensional Data Analysis with Low-Dimensional Models:
Principles, Computation, and Applications},
publisher = {Cambridge University Press},
year = {2022}
}
Catalog Links
The book hardcopy is available for order at the following sites, respectively:
Additional Resources for Instructors and Readers
Related teaching and learning materials can be found at:
For instructors: if you plan to use the textbook for your courses, please contact us for access to teaching material such as exercises and solutions,
including code for programming exercises. You may also request for editable latex files for the lecture slides to help make your own.
Please send us your course websites and materials to be shared here.
Reader's feedback: If you have any feedback, suggestions, or
errors to report, please send us an email at: book.wright.ma@gmail.com.
Book errata: TBA.
Some older course websites:
©2020 Yi Ma
Last modified: Sun December 4 11:15:08 PST 2021