CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
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DOI：
10.1016/j.acha.2008.07.002
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发表时间：
2009-05-01
影响因子：
2.5
通讯作者：
Tropp, J. A.
中科院分区：
文献类型：
--
作者：
Needell, D.;Tropp, J. A.
压缩采样为获取相对于正交基可压缩的信号提供了一种新的范式。压缩采样中的主要算法挑战是从含噪样本中近似一个可压缩信号。本文描述了一种新的迭代恢复算法，称为压缩采样匹配追踪（CoSaMP），它提供了与基于优化的最佳方法相同的保证。此外，该算法对计算成本和存储给出了严格的界限。对于实际问题它可能极其高效，因为它只需要与采样矩阵进行矩阵 - 向量乘法。对于可压缩信号，运行时间仅为\(C(N\log_{2}N)\)，其中\(N\)是信号的长度。由爱思唯尔公司出版。
Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery algorithm called CoSaMP that delivers the same guarantees as the best optimization-based approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix-vector multiplies with the sampling matrix. For compressible signals, the running time is just C(N log(2) N), where N is the length of the signal. Published by Elsevier Inc.