Abstract : Several computationally efficient algorithms have been shown to offer near optimal recovery of sparse signals from a small number of linear measurements. However, whilst many of the methods have similar guarantees whenever the measurements satisfy the so called restricted isometry property, empirical performance of the methods can vary significantly in a regime in which this condition is not satisfied. We here modify the Iterative Hard Thresholding algorithm by including an automatic step-size calculation. This makes the method independent from an arbitrary scaling of the measurement system and leads to a method that shows state of the art empirical performance. What is more, theoretical guarantees derived for the unmodified algorithm carry over to the new method with only minor changes.
Type de document :
Communication dans un congrès
Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009
https://hal.inria.fr/inria-00369654
Contributeur : Ist Rennes
<>
Soumis le : vendredi 20 mars 2009 - 15:43:50
Dernière modification le : jeudi 26 octobre 2017 - 16:34:02
Document(s) archivé(s) le : jeudi 10 juin 2010 - 17:52:08
Thomas Blumensath, Michael E Davies. How to use the iterative hard thresholding algorithm. Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009. 〈inria-00369654〉
