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Conference Papers Year : 2014

Sparse representations in nested non-linear models


Following recent contributions in non-linear sparse represen-tations, this work focuses on a particular non-linear model, defined as the nested composition of functions. Recalling that most linear sparse representation algorithms can be straight-forwardly extended to non-linear models, we emphasize that their performance highly relies on an efficient computation of the gradient of the objective function. In the particular case of interest, we propose to resort to a well-known technique from the theory of optimal control to estimate the gradient. This computation is then implemented into the optimization procedure proposed byCan es et al., leading to a non-linear extension of it.
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hal-01096254 , version 1 (17-12-2014)



Angélique Drémeau, Patrick Héas, Cédric Herzet. Sparse representations in nested non-linear models. IEEE International Conference on Speech, Acoustic and Signal Processing (ICASSP), May 2014, Firenze, Italy. ⟨10.1109/ICASSP.2014.6855147⟩. ⟨hal-01096254⟩
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