High-Dimensional Data Analysis with Low-Dimensional Models
«'At the very core of our ability to process data stands the fact that sources of information are structured. Modeling data, explicitly or implicitly, is our way of exposing this structure and exploiting it, being the essence of the fields of signal and image processing and machine learning. The past two decades have brought a revolution to our understanding of these facts, and this 'must-read' book provides the foundations of these recent developments, covering theoretical, numerical, and applicative aspects of this field in a thorough and clear manner.' Michael Elad, Technion – Israel Institute of Technology»
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. Les mer
Detaljer
- Forlag
- Cambridge University Press
- Innbinding
- Innbundet
- Språk
- Engelsk
- ISBN
- 9781108489737
- Utgivelsesår
- 2022
- Format
- 25 x 18 cm
Anmeldelser
«'At the very core of our ability to process data stands the fact that sources of information are structured. Modeling data, explicitly or implicitly, is our way of exposing this structure and exploiting it, being the essence of the fields of signal and image processing and machine learning. The past two decades have brought a revolution to our understanding of these facts, and this 'must-read' book provides the foundations of these recent developments, covering theoretical, numerical, and applicative aspects of this field in a thorough and clear manner.' Michael Elad, Technion – Israel Institute of Technology»
«'Students will learn a lot from reading this book … They will learn about mathematical reasoning, they will learn about data models and about connecting those to reality, and they will learn about algorithms. The book also contains computer scripts so that we can see ideas in action, and carefully crafted exercises making it perfect for upper-level undergraduate or graduate-level instruction. The breadth and depth make this a reference for anyone interested in the mathematical foundations of data science.' Emmanuel Candès, Stanford University (from the foreword)»