| 书目名称 | Compressed Sensing & Sparse Filtering |
| 编辑 | Avishy Y. Carmi,Lyudmila Mihaylova,Simon J. Godsil |
| 视频video | |
| 概述 | Presents fundamental concepts, methods and algorithms able to cope with undersampled data.Introduces compressive sampling, called also compressed sensing..Written by well-known experts in the field.In |
| 丛书名称 | Signals and Communication Technology |
| 图书封面 |  |
| 描述 | .This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations thanconventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections |
| 出版日期 | Book 2014 |
| 关键词 | Bayesian approach; L1-norm penalties; compressive sampling; compressive sensing; homotopy-type solutions |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-38398-4 |
| isbn_softcover | 978-3-662-50894-7 |
| isbn_ebook | 978-3-642-38398-4Series ISSN 1860-4862 Series E-ISSN 1860-4870 |
| issn_series | 1860-4862 |
| copyright | Springer-Verlag Berlin Heidelberg 2014 |
发表于 2025-3-21 18:02:01
