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Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding
2011
EURASIP Journal on Advances in Signal Processing
Parameterized quadratic programming (Lasso) is a powerful tool for the recovery of sparse signals based on underdetermined observations contaminated by noise. In this paper, we study the problem of simultaneous sparsity pattern recovery and approximation recovery based on the Lasso. An extended Lasso method is proposed with the following main contributions: (1) we analyze the recovery accuracy of Lasso under the condition of guaranteeing the recovery of nonzero entries positions. Specifically,
doi:10.1155/2011/528734
fatcat:bgprw3xoc5eefdl6h3n5mg5kcu