High-Dimensional Data Analysis with Low-Dimensional Models Principles, Computation, and Applications
- Textbook
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…
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Key features
- Bridges the gap between principles and applications of low-dimensional models for high-dimensional data analysis
- Covers a wide range of application areas
- Accompanied online by code
- Foreword by Emmanuel Candès
About the book
- DOI https://doi.org/10.1017/9781108779302
- Subjects Communications and Signal Processing,Computer Science,Engineering,Machine Learning and Pattern Recognition
- Format: Hardback
- Publication date: 07 April 2022
- ISBN: 9781108489737
- Dimensions (mm): 244 x 170 mm
- Weight: 1.43kg
- Page extent: 650 pages
- Availability: Available
- Format: Digital
- Publication date: 11 March 2022
- ISBN: 9781108779302
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