High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

Wright, John; Ma, Yi

ISBN 10: 1108489737 / ISBN 13: 9781108489737

Published by Cambridge University Press, 2022

About this Item

This book is in good condition. The cover has minor creases or bends. The binding is tight and pages are intact. Some pages may have writing or highlighting.

Seller Inventory # BRV.1108489737.G

Bibliographic Details

• Title: High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
• Publisher: Cambridge University Press
• Publication Date: 2022
• Language: English
• ISBN 10: 1108489737
• ISBN 13: 9781108489737
• Binding: Hardcover
• Condition: good

About this title

Synopsis

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

About the Authors

John Wright is an Associate Professor in the Electrical Engineering Department and the Data Science Institute at Columbia University.

Yi Ma is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a Fellow of the IEEE, ACM, and SIAM.