Abstract : —The applicability of many signal processing and data analysis techniques is limited by their prohibitive computational complexity. The cost of such techniques is often dominated by the application of large linear operators. This short paper introduces an algorithm aimed at reducing the complexity of applying such operators by approximately fac-torizing the corresponding matrix into few sparse factors. The proposed approach, which relies on recent advances in non-convex optimization, is first exposed, and then demonstrated experimentally.
Type de document :
Document associé à des manifestations scientifiques
SPARS 2015 Signal Processing with Adaptive Sparse Structured Representations, Jul 2015, Cambridge, United Kingdom
https://hal.inria.fr/hal-01158057
Contributeur : Luc Le Magoarou
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Soumis le : vendredi 29 mai 2015 - 13:49:45
Dernière modification le : mercredi 16 mai 2018 - 11:24:07
Document(s) archivé(s) le : mardi 15 septembre 2015 - 08:16:53
