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CISS
2008
IEEE
15 years 4 months ago
On sparse representations of linear operators and the approximation of matrix products
—Thus far, sparse representations have been exploited largely in the context of robustly estimating functions in a noisy environment from a few measurements. In this context, the...
Mohamed-Ali Belabbas, Patrick J. Wolfe
CORR
2010
Springer
154views Education» more  CORR 2010»
14 years 9 months ago
Improved Approximation Guarantees for Sublinear-Time Fourier Algorithms
ABSTRACT. In this paper modified variants of the sparse Fourier transform algorithms from [14] are presented which improve on the approximation error bounds of the original algorit...
M. A. Iwen
CORR
2011
Springer
148views Education» more  CORR 2011»
14 years 4 months ago
How well can we estimate a sparse vector?
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on t...
Emmanuel J. Candès, Mark A. Davenport
ICCAD
2007
IEEE
173views Hardware» more  ICCAD 2007»
15 years 6 months ago
Bounding L2 gain system error generated by approximations of the nonlinear vector field
Abstract— Typical nonlinear model order reduction approaches need to address two issues: reducing the order of the model, and approximating the vector field. In this paper we fo...
Kin Cheong Sou, Alexandre Megretski, Luca Daniel
CORR
2011
Springer
202views Education» more  CORR 2011»
14 years 4 months ago
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
We analyze a class of estimators based on a convex relaxation for solving highdimensional matrix decomposition problems. The observations are the noisy realizations of the sum of ...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...