Accelerated Dictionary Learning for Sparse Signal Representation

Abstract : Learning sparsifying dictionaries from a set of training signals has been shown to have much better performance than pre-designed dictionaries in many signal processing tasks, including image enhancement. To this aim, numerous practical dictionary learning (DL) algorithms have been proposed over the last decade. This paper introduces an accelerated DL algorithm based on iterative proximal methods. The new algorithm efficiently utilizes the iterative nature of DL process, and uses accelerated schemes for updating dictionary and coefficient matrix. Our numerical experiments on dictionary recovery show that, compared with some well-known DL algorithms, our proposed one has a better convergence rate. It is also able to successfully recover underlying dictionaries for different sparsity and noise levels.
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Communication dans un congrès
Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lectures Notes in Computer Sciences (10169), pp.531 - 541, 2017, Latent Variable Analysis and Signal Separation. 〈10.1007/978-3-319-53547-0_50〉
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Dernière modification le : lundi 9 avril 2018 - 12:22:44
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Fateme Ghayem, Mostafa Sadeghi, Massoud Babaie-Zadeh, Christian Jutten. Accelerated Dictionary Learning for Sparse Signal Representation. Petr Tichavsky; Massoud Babaie-Zadeh; Olivier Michel; Nadège Thirion-Moreau. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. Springer, Lectures Notes in Computer Sciences (10169), pp.531 - 541, 2017, Latent Variable Analysis and Signal Separation. 〈10.1007/978-3-319-53547-0_50〉. 〈hal-01479437〉

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