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Compressed Sensing and Its Applications

Third International MATHEON Conference 2017

  • Holger Boche
  • Giuseppe Caire
  • Robert Calderbank
  • Gitta Kutyniok
  • Rudolf Mathar
  • Philipp Petersen
Book

Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Niklas Koep, Arash Behboodi, Rudolf Mathar
    Pages 1-65
  3. Sjoerd Dirksen
    Pages 67-95
  4. Robert Calderbank, Anders Hansen, Bogdan Roman, Laura Thesing
    Pages 97-128
  5. Denali Molitor, Deanna Needell, Aaron Nelson, Rayan Saab, Palina Salanevich
    Pages 129-151
  6. Daniel Jakubovitz, Raja Giryes, Miguel R. D. Rodrigues
    Pages 153-193
  7. Peter Maass
    Pages 195-209
  8. Andreas Elsener, Sara van de Geer
    Pages 211-236
  9. Yuejie Chi, Yuanxin Li, Huishuai Zhang, Yingbin Liang
    Pages 237-261
  10. Martin Burger, Janic Föcke, Lukas Nickel, Peter Jung, Sven Augustin
    Pages 263-290
  11. Back Matter
    Pages 291-295

About this book

Introduction

The chapters in this volume highlight the state-of-the-art of compressed sensing and are based on talks given at the third international MATHEON conference on the same topic, held from December 4-8, 2017 at the Technical University in Berlin. In addition to methods in compressed sensing, chapters provide insights into cutting edge applications of deep learning in data science, highlighting the overlapping ideas and methods that connect the fields of compressed sensing and deep learning. Specific topics covered include:
  • Quantized compressed sensing
  • Classification
  • Machine learning
  • Oracle inequalities
  • Non-convex optimization
  • Image reconstruction
  • Statistical learning theory
This volume will be a valuable resource for graduate students and researchers in the areas of mathematics, computer science, and engineering, as well as other applied scientists exploring potential applications of compressed sensing.

Keywords

Compressed sensing Compressed sensing theory and applications Compressed sensing book Compressed sensing introduction Compressed sensing 2019 Deep learning compressed sensing Deep learning book Machine learning Quantized compressed sensing Signal sensing book Generalization error machine learning MATHEON conference

Editors and affiliations

  • Holger Boche
    • 1
  • Giuseppe Caire
    • 2
  • Robert Calderbank
    • 3
  • Gitta Kutyniok
    • 4
  • Rudolf Mathar
    • 5
  • Philipp Petersen
    • 6
  1. 1.Department of Electrical and Computer Engineering, Munich Center for Quantum Science and Technology (MCQST)Technical University of MunichMunichGermany
  2. 2.Institute of Telecommunications SystemsTechnical University of BerlinBerlinGermany
  3. 3.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA
  4. 4.Department of MathematicsTechnical University of BerlinBerlinGermany
  5. 5.Faculty of Electrical Engineering and Information TechnologyRWTH Aachen UniversityAachenGermany
  6. 6.Mathematical InstituteUniversity of OxfordOxfordUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73074-5
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Birkhäuser, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-73073-8
  • Online ISBN 978-3-319-73074-5
  • Series Print ISSN 2296-5009
  • Series Online ISSN 2296-5017
  • Buy this book on publisher's site