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Conference papers

Sparsity Measure and the Detection of Significant Data

Abdourrahmane Atto 1, 2 Dominique Pastor 1, 2 Grégoire Mercier 2, 3 
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The paper provides a formal description of the sparsity of a representation via the detection thresholds. The formalism proposed derives from theoretical results about the detection of significant coefficients when data are observed in presence of additive white Gaussian noise. The detection thresholds depend on two parameters describing the sparsity degree for the representation of a signal. The standard universal and minimax thresholds correspond to detection thresholds associated with different sparsity degrees.
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Submitted on : Friday, March 20, 2009 - 3:10:52 PM
Last modification on : Monday, March 14, 2022 - 11:08:11 AM
Long-term archiving on: : Thursday, June 10, 2010 - 5:50:21 PM


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  • HAL Id : inria-00369628, version 1


Abdourrahmane Atto, Dominique Pastor, Grégoire Mercier. Sparsity Measure and the Detection of Significant Data. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369628⟩



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