Turbo Message Passing Algorithms for Structured Signal Recovery / SpringerBriefs in Computer Science (PDF)
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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
Zhipeng Xue received the B.E. degree in communication engineering from Southwest Jiaotong University, China, in 2015. He is currently pursuing the Ph.D. degree at ShanghaiTech University, in the School of Information Science and Technology. His research interests include statistical signal processing and machine learning.
- Autoren: Xiaojun Yuan , Zhipeng Xue
- 2020, 1st ed. 2020, 105 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030547620
- ISBN-13: 9783030547622
- Erscheinungsdatum: 13.10.2020
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- Dateiformat: PDF
- Grösse: 3.04 MB
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