Orthogonal Matching Pursuit with Noisy and Missing Data: Low and High Dimensional Results

Measurement error models

W. A. Fuller · 1987

Earlier work this paper cites.

Greed is good: Algorithmic results for sparse approximation

J. A. Tropp · 2004

Earlier work this paper cites.

Measurement error in nonlinear models: a modern perspective

R. J. Carroll · 2006

Earlier work this paper cites.

Stable recovery of sparse overcomplete representations in the presence of noise

D. L. Donoho, M. Elad, and V. N. Temlyakov · 2006

Earlier work this paper cites.

Signal recovery from random measurements via orthogonal matching pursuit

J. A. Tropp and A. C. Gilbert · 2007

Earlier work this paper cites.

Covariate selection for linear errors-in-variables regression models

Original

Q. Xu and J. You · 2007

Earlier work this paper cites.

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A. K. Fletcher and S. Rangan · 2009

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M. A. Davenport and M. B. Wakin · 2010

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M. Rosenbaum and A. B. Tsybakov · 2010

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Improved matrix uncertainty selector

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