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Xue / Yuan

Turbo Message Passing Algorithms for Structured Signal Recovery

Medium: Buch
ISBN: 978-3-030-54761-5
Verlag: Springer International Publishing
Erscheinungstermin: 14.10.2020
Lieferfrist: bis zu 10 Tage

This book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. 

  • Provides an in depth look into turbo message passing algorithms for structured signal recovery
  • Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing
  • Shows applications in areas such as wireless communications and computer vision


Produkteigenschaften


  • Artikelnummer: 9783030547615
  • Medium: Buch
  • ISBN: 978-3-030-54761-5
  • Verlag: Springer International Publishing
  • Erscheinungstermin: 14.10.2020
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2020
  • Serie: SpringerBriefs in Computer Science
  • Produktform: Kartoniert
  • Gewicht: 195 g
  • Seiten: 105
  • Format (B x H x T): 155 x 235 x 7 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Introduction.- Turbo Message Passing for Compressed Sensing.- Turbo Message Passing for Affine Rank Minimization.- Turbo Message Passing for Compressed Robust Principal Component Analysis.- Learned Turbo Message Passing Algorithms.- Future Research Directions.- Conclusion.