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Turbo Message Passing Algorithms for Structured Signal Recovery. 1st ed. 2020
・ISBN 978-3-030-54761-5 paper EUR 59.99
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| 著者・編者 | Yuan, Xiaojun / Xue, Zhipeng, |
|---|---|
| シリーズ | (SpringerBriefs in Computer Science) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2020 |
| ページ数 | 105 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-82905＞ |
解説
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