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conference paper
Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term
2005
Proc. of Workshop on Signal Processing with Adaptative Sparse Structured Representations
This paper studies the problem of sparse signal approximation over redundant dictionaries. Our attention is focused on the minimization of a cost function where the error is measured by using the L1 norm, giving thus less importance to outliers. We show a constructive equivalence between the proposed minimization problem and Linear Programming. A recovery condition is then provided and an example illustrates the use of such a technique for denoising.
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Granai2005_1403.pdf
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openaccess
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120.92 KB
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Adobe PDF
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