Sparse Recovery Using Sparse Matrices

IF 25.9 1区计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Proceedings of the IEEE Pub Date : 2010-03-10 DOI:10.1109/JPROC.2010.2045092

Anna Gilbert;Piotr Indyk

引用次数: 400

Abstract

In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas, including compressive sensing, data stream computing, and group testing.

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稀疏矩阵的稀疏恢复

在本文中，我们研究了基于稀疏随机矩阵的稀疏恢复问题的算法。这样的矩阵具有几个有吸引力的特性：它们支持计算复杂度低的算法，并使对信号执行增量更新变得容易。我们讨论了几个领域的应用，包括压缩传感、数据流计算和组测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the IEEE 工程技术-工程：电子与电气

CiteScore

46.40

自引率

1.00%

发文量

160

审稿时长

3-8 weeks

期刊介绍： Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.

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