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Dictionary learning with spatio-spectral sparsity constraints

Abstract : Devising efficient sparse decomposition algorithms in large redundant dictionaries has attracted much attention recently. However, choosing the right dictionary for a given data set remains an issue. An interesting approach is to learn the best dictionary from the data itself. The purpose of this contribution is to describe a new dictionary learning algorithm for multichannel data analysis purposes under specific assumptions. We assume a large number of contiguous channels as in so-called hyperspectral data. In this case it makes sense to consider a priori that the collected data exhibits sparse spectral signatures and sparse spatial morphologies in specified dictionaries of spectral and spatial waveforms. Building on GMCA, the proposed algorithm gives a practical way to enforce the additional a priori spectral sparsity constraint on the dictionary space. Numerical experiments with synthetic and real hyperspectral data illustrate the efficiency of the proposed algorithm.
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https://hal.inria.fr/inria-00369488
Contributor : Ist Rennes <>
Submitted on : Friday, March 20, 2009 - 9:36:46 AM
Last modification on : Thursday, July 1, 2021 - 4:36:02 PM
Long-term archiving on: : Thursday, June 10, 2010 - 5:25:25 PM

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  • HAL Id : inria-00369488, version 1

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Yassir Moudden, Jérome Bobin, Jean-Luc Starck, Jalal M. Fadili. Dictionary learning with spatio-spectral sparsity constraints. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369488⟩

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