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report
Sparse Approximation by Linear Programming: Measuring the Error with the $ell_1$ Norm
2005
In this report we study the problem of sparse signal approximation over redundant dictionaries. We focus our attention on the minimization of a cost function where the error is measured using a l1 norm. We show a constructive equivalence between this minimization and Linear Programming. A recovery condition is then proved and finally we provide an example of the use of such a technique for denoising.
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Granai2005_1295.pdf
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openaccess
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128.71 KB
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Adobe PDF
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