Infoscience

Report

Sparse Approximation by Linear Programming: Measuring the Error with the $ell_1$ Norm

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.

    Keywords: LTS2

    Note:

    ITS

    Reference

    • EPFL-REPORT-87170

    Record created on 2006-06-14, modified on 2016-08-08

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