Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding
نویسندگان
چکیده
منابع مشابه
Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding
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 ...
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ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2011
ISSN: 1687-6180
DOI: 10.1155/2011/528734