CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples
CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples
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DOI：
10.1145/1859204.1859229
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发表时间：
2010-12-01
影响因子：
22.7
通讯作者：
Tropp, Joel A.
中科院分区：
文献类型：
--
作者：
Needell, Deanna;Tropp, Joel A.
压缩采样（COSA）是用于开发数据采样技术的新范式。它基于以下原则：许多类型的矢量空间数据是可压缩的，这是数学信号处理中的艺术术语。关键的想法是，随机尺寸缩小将信息保存在可压缩信号中，并且可以开发有效实施此维度降低的硬件设备。 COSA中的主要计算挑战是从采样设备获得的简化表示形式中重建可压缩信号。该扩展的摘要描述了一种称为COSAMP的算法，该算法完成了数据恢复任务。这是第一个为资源使用提供近乎最佳保证的已知方法。
Compressive sampling (CoSa) is a new paradigm for developing data sampling technologies. It is based on the principle that many types of vector-space data are compressible, which is a term of art in mathematical signal processing. The key ideas are that randomized dimension reduction preserves the information in a compressible signal and that it is possible to develop hardware devices that implement this dimension reduction efficiently. The main computational challenge in CoSa is to reconstruct a compressible signal from the reduced representation acquired by the sampling device. This extended abstract describes a recent algorithm, called CoSaMP, that accomplishes the data recovery task. It was the first known method to offer near-optimal guarantees on resource usage.