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Turbo Message Passing Algorithms for Structured Signal Recovery [electronic resource] / by Xiaojun Yuan, Zhipeng Xue.

Author/Creator:
Yuan, Xiaojun. author., Author,
Publication:
Cham : Springer International Publishing : Imprint: Springer, 2020.
Format/Description:
Book
1 online resource (XI, 105 pages) : 30 illustrations, 20 illustrations in color.
Edition:
1st ed. 2020.
Series:
Computer Science (SpringerNature-11645)
SpringerBriefs in computer science 2191-5768
SpringerBriefs in Computer Science, 2191-5768
Contained In:
Springer Nature eBook
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Other Title:
a
Subjects:
Electrical engineering.
Signal processing.
Image processing.
Speech processing systems.
Computer networks.
Local subjects:
Communications Engineering, Networks. (search)
Signal, Image and Speech Processing. (search)
Computer Communication Networks. (search)
System Details:
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Summary:
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.
Contents:
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.
Contributor:
Xue, Zhipeng. author., Author,
SpringerLink (Online service)
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Printed edition:
Printed edition:
ISBN:
978-3-030-54762-2
9783030547622
Publisher Number:
10.1007/978-3-030-54762-2 doi
Access Restriction:
Restricted for use by site license.