Turbo message passing algorithms for structured signal recovery
著者
書誌事項
Turbo message passing algorithms for structured signal recovery
（SpringerBriefs in computer science）
Springer, c2020
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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
目次
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.
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