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.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/inria-00369577
Contributor : Ist Rennes <>
Submitted on : Friday, March 20, 2009 - 1:35:14 PM
Last modification on : Wednesday, October 10, 2018 - 1:26:44 AM
Long-term archiving on: Friday, October 12, 2012 - 2:01:24 PM

File

30.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00369577, version 1

Collections

Citation

Matthieu Kowalski, Bruno Torrésani. Structured Sparsity: from Mixed Norms to Structured Shrinkage. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369577⟩

Share

Metrics

Record views

490

Files downloads

868