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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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Submitted on : Friday, March 20, 2009 - 9:36:46 AM
Last modification on : Saturday, June 25, 2022 - 9:46:01 AM
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  • HAL Id : inria-00369488, version 1


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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