John Wright (New York Columbia University), Yi Ma (Berkeley University of California)

High-Dimensional Data Analysis with Low-Dimensional Models

Principles, Computation, and Applications

• Gebundenes Buch

Produktbeschreibung

A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in computer science, data science, and electrical engineering. With foreword by Emmanuel Candà s.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 650
• Erscheinungstermin: 7. April 2022
• Englisch
• Abmessung: 250mm x 172mm x 38mm
• Gewicht: 1496g
• ISBN-13: 9781108489737
• ISBN-10: 1108489737
• Artikelnr.: 62162787

Autorenporträt

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

Inhaltsangabe

Foreword
Preface
Acknowledgements
1. Introduction
Part I. Principles of Low-Dimensional Models: 2. Sparse Signal Models
3. Convex Methods for Sparse Signal Recovery
4. Convex Methods for Low-Rank Matrix Recovery
5. Decomposing Low-Rank and Sparse Matrices
6. Recovering General Low-Dimensional Models
7. Nonconvex Methods for Low-Dimensional Models
Part II. Computation for Large-Scale Problems: 8. Convex Optimization for Structured Signal Recovery
9. Nonconvex Optimization for High-Dimensional Problems
Part III. Applications to Real-World Problems: 10. Magnetic Resonance Imaging
11. Wideband Spectrum Sensing
12. Scientific Imaging Problems
13. Robust Face Recognition
14. Robust Photometric Stereo
15. Structured Texture Recovery
16. Deep Networks for Classification
Appendices: Appendix A. Facts from Linear Algebra and Matrix Analysis
Appendix B. Convex Sets and Functions
Appendix C. Optimization Problems and Optimality Conditions
Appendix D. Methods for Optimization
Appendix E. Facts from High-Dimensional Statistics
Bibliography
List of Symbols
Index.