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出版者:Cambridge University Press

作者:Eldar, Yonina C.; Kutyniok, Gitta;

出品人:

頁數:558

译者:

出版時間:2012-6-29

價格:USD 108.00

裝幀:Hardcover

isbn號碼:9781107005587

叢書系列:

圖書標籤:

數學
壓縮感知
計算機科學
壓縮感知-稀疏錶示-矩陣分解
機器學習
計算機
科普
壓縮
壓縮感知
稀疏信號處理
信號處理
信息論
數學
工程
算法
機器學習
優化
統計學

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具體描述

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

著者簡介

圖書目錄

1.Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok;
2.Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann and Guoshen Yu;
3.Xampling: compressed sensing of analog signals Moshe Mishali and Yonina C. Eldar;
4.Sampling at the rate of innovation: theory and applications Jose Antonia Uriguen, Yonina C. Eldar, Pier Luigi Dragotta and Zvika Ben-Haim;
5.Introduction to the non-asymptotic analysis of random matrices Roman Vershynin;
6.Adaptive sensing for sparse recovery Jarvis Haupt and Robert Nowak;
7.Fundamental thresholds in compressed sensing: a high-dimensional geometry approach Weiyu Xu and Babak Hassibi;
8.Greedy algorithms for compressed sensing Thomas Blumensath, Michael E. Davies and Gabriel Rilling;
9.Graphical models concepts in compressed sensing Andrea Montanari;
10.Finding needles in compressed haystacks Robert Calderbank, Sina Jafarpour and Jeremy Kent;
11.Data separation by sparse representations Gitta Kutyniok;
12.Face recognition by sparse representation Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma and John Wright.
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