• Publications
  • Influence
Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
  • T. T. Cai, L. Wang
  • Computer Science
  • IEEE Transactions on Information Theory
  • 1 July 2011
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedyExpand
  • 709
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Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors areExpand
  • 279
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Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors areExpand
  • 171
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New Bounds for Restricted Isometry Constants
This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an n × p real matrix and A; be a positive integer with k ≤ n. One of the main results of this paperExpand
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Stable Recovery of Sparse Signals and an Oracle Inequality
This article considers sparse signal recovery in the presence of noise. A mutual incoherence condition which was previously used for exact recovery in the noiseless case is shown to be sufficient forExpand
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Shifting Inequality and Recovery of Sparse Signals
In this paper, we present a concise and coherent analysis of the constrained ¿1 minimization method for stable recovering of high-dimensional sparse signals both in the noiseless case and noisy case.Expand
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The L1L1 penalized LAD estimator for high dimensional linear regression
  • L. Wang
  • Computer Science, Mathematics
  • J. Multivar. Anal.
  • 1 September 2013
In this paper, the high-dimensional sparse linear regression model is considered, where the overall number of variables is larger than the number of observations. We investigate the L"1 penalizedExpand
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Pivotal estimation via square-root Lasso in nonparametric regression
We propose a self-tuning $\sqrt{\mathrm {Lasso}}$ method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale,Expand
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Orthogonal Matching Pursuit for Sparse Signal Recovery
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedyExpand
  • 37
  • 10
TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models
We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very fewExpand
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