| High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications Contributor(s): Wright, John (Author), Ma, Yi (Author) |
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ISBN: 1108489737 ISBN-13: 9781108489737 Publisher: Cambridge University Press OUR PRICE: $79.79 Product Type: Hardcover - Other Formats Published: April 2022 |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Signals & Signal Processing - Computers | Computer Vision & Pattern Recognition |
| Physical Information: 1.4" H x 7" W x 9.79" (3.10 lbs) 650 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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 electrical engineering, computer science and data science, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès. |
