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We propose a method for high dimensional sparse estimation in the multiple measurement vector case. The method is based on the covariance matching technique and with a sparse penalty along the ideas of the square-root LASSO (sr-LASSO). The method not only benefits from the strong characteristics of sr-LASSO (independence of the hyper-parameter selection from the noise variance), but also offers a...
We derive a linear minimum mean square error estimator for sparse vector estimation from an underdetermined set of linear equations. The derivation of the estimator uses a prior distribution conditioned on the support set of the underlying sparse vector. The estimator is used in the architecture of the standard orthogonal matching pursuit algorithm to achieve a better performance.
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