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Journal Articles IEEE Signal Processing Letters Year : 2017

Learning Dictionaries as a sum of Kronecker products

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Abstract

The choice of an appropriate frame, or dictionary, is a crucial step in the sparse representation of a given class of signals. Traditional dictionary learning techniques generally lead to unstructured dictionaries which are costly to deploy and train, and do not scale well to higher dimensional signals. In order to overcome such limitation, we propose a learning algorithm that constrains the dictionary to be a sum of Kronecker products of smaller sub-dictionaries. This approach, named SuKro, is demonstrated experimentally on an image denoising application.
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Dates and versions

hal-01672349 , version 1 (24-12-2017)

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Cassio Fraga Dantas, Michele Nazareth da Costa, Renato da Rocha Lopes. Learning Dictionaries as a sum of Kronecker products. IEEE Signal Processing Letters, 2017, 24 (5), pp.559 - 563. ⟨10.1109/LSP.2017.2681159⟩. ⟨hal-01672349⟩
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