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

This textbook provides a comprehensive introduction to mathematical models for high-dimensional data analysis,covering principles,algorithms,and applications,with code available online. It is ideal for students in computer science,data science,and electrical engineering,and those studying sparsity,low-dimensional structures,and high-dimensional data.

Format: Hardback
Length: 650 pages
Publication date: 13 January 2022
Publisher: Cambridge University Press

This comprehensive and rigorous introduction delves into the core principles, algorithms, and applications of essential mathematical models for high-dimensional data analysis. Encompassing a wide range of low-dimensional models and analytical techniques, including sparse and low-rank models, convex and non-convex formulations, and more, it offers a unified coverage that spans diverse fields. By providing readers with practical insights and hands-on experience, this text equips them with the skills to develop efficient and scalable algorithms for solving real-world problems. Numerous examples and exercises reinforce the theoretical concepts, while the inclusion of code available online further enhances the learning experience. Suitable for senior and graduate students in computer science, data science, electrical engineering, and courses related to sparsity, low-dimensional structures, and high-dimensional data, this textbook serves as a valuable resource for advancing knowledge in this rapidly evolving field.

A preface by Emmanuel Candès adds an insightful perspective to the text.

Weight: 1448g
Dimension: 177 x 251 x 39 (mm)
ISBN-13: 9781108489737
Edition number: New ed