In linear algebra, the restricted isometry property characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors… (More)

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2015

2015

- Alexander Barg, Arya Mazumdar, Rongrong Wang
- IEEE Transactions on Information Theory
- 2015

We study statistical restricted isometry, a property closely related to sparse signal recovery, of deterministic sensing matrices… (More)

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2013

Highly Cited

2013

- Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, William F. Sawin
- IEEE Transactions on Information Theory
- 2013

This paper is concerned with an important matrix condition in compressed sensing known as the restricted isometry property (RIP… (More)

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2012

Highly Cited

2012

- Felix Krahmer, Shahar Mendelson, Holger Rauhut
- ArXiv
- 2012

We present a new bound for suprema of a special type of chaos processes indexed by a set of matrices, which is based on a… (More)

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2011

Highly Cited

2011

- Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner
- SIAM Review
- 2011

Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N measurements; it posits that… (More)

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2010

Highly Cited

2010

- Mark A. Davenport, Michael B. Wakin
- IEEE Transactions on Information Theory
- 2010

Orthogonal matching pursuit (OMP) is the canonical greedy algorithm for sparse approximation. In this paper we demonstrate that… (More)

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2009

2009

- Leslie Ying, Yi Ming Zou
- 2009 IEEE International Conference on Acoustics…
- 2009

The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in compressed sensing theory. It… (More)

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2008

Highly Cited

2008

It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of… (More)

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2008

2008

Compressive Sampling (CS) describes a method for reconstructing high-dimensional sparse signals from a small number of linear… (More)

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2007

Highly Cited

2007

We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices… (More)

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2007

Highly Cited

2007

In previous work, numerical experiments showed that ` minimization with 0 < p < 1 recovers sparse signals from fewer linear… (More)

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