Sparse Approximation by Linear Programming using an L1 Data-Fidelity Term

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.


Published in:
Proc. of Workshop on Signal Processing with Adaptative Sparse Structured Representations
Year:
2005
Publisher:
IEEE
Keywords:
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 Record created 2006-06-14, last modified 2018-03-17

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