Structured Sparsity: from Mixed Norms to Structured Shrinkage

Abstract : Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present contribution lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps. These generalize Group LASSO and the previously introduced Elitist LASSO by introducing more flexibility in the coefficient domain modeling. We study experimentally the performances of corresponding shrinkage operators in terms of significance map estimation in the orthogonal basis case. We also study their performance in the overcomplete situation, using iterative thresholding.
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Communication dans un congrès
Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009
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Matthieu Kowalski, Bruno Torrésani. Structured Sparsity: from Mixed Norms to Structured Shrinkage. Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009. 〈inria-00369577〉

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