國立高雄大學圖資館 |

Language: English

Back

Turbo message passing algorithms for...

SpringerLink (Online service)

Turbo message passing algorithms for structured signal recovery

Record Type:	Electronic resources : Monograph/item
Title/Author:	Turbo message passing algorithms for structured signal recoveryby 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.
Contained By:	Springer Nature eBook
Subject:	Compressed sensing (Telecommunication)
Online resource:	https://doi.org/10.1007/978-3-030-54762-2
ISBN:	9783030547622$q(electronic bk.)

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$q(electronic bk.)

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

769143
Compressed sensing (Telecommunication)

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Turbo message passing algorithms for structured signal recovery
LDR:02619nmm a2200337 a 4500 001 588779
003 DE-He213
005 20201013184953.0
006 m d
007 cr nn 008maaau
008 210525s2020 sz s 0 eng d
020 $a 9783030547622$q(electronic bk.)
020 $a 9783030547615$q(paper)
024 7 $a 10.1007/978-3-030-54762-2 $2 doi
035 $a 978-3-030-54762-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5102.9
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.3822 $2 23
090 $a TK5102.9 $b .Y94 2020
100 1 $a Yuan, Xiaojun. $3 839081
245 1 0 $a Turbo message passing algorithms for structured signal recovery $h [electronic resource] / $c by Xiaojun Yuan, Zhipeng Xue.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xi, 105 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
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.
650 0 $a Compressed sensing (Telecommunication) $3 769143
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Computer Communication Networks. $3 218087
700 1 $a Xue, Zhipeng. $3 880329
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u https://doi.org/10.1007/978-3-030-54762-2
950 $a Computer Science (SpringerNature-11645)