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Turbo message passing algorithms for...

Yuan, Xiaojun.

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Turbo message passing algorithms for structured signal recovery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Turbo message passing algorithms for structured signal recovery/ by Xiaojun Yuan, Zhipeng Xue.
Author:	Yuan, Xiaojun.
other author:	Xue, Zhipeng.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xi, 105 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication) -
Online resource:	https://doi.org/10.1007/978-3-030-54762-2
ISBN:	9783030547622

Turbo message passing algorithms for structured signal recovery
Yuan, Xiaojun.

Turbo message passing algorithms for structured signal recovery[electronic resource] /by Xiaojun Yuan, Zhipeng Xue. - Cham :Springer International Publishing :2020. - xi, 105 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

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.

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.

ISBN: 9783030547622

Standard No.: 10.1007/978-3-030-54762-2doiSubjects--Topical Terms:

3214582
Compressed sensing (Telecommunication)

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Turbo message passing algorithms for structured signal recovery
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505 0 $a 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.
520 $a 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.
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