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
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