Artículos de revistas
Learning Dictionaries As A Sum Of Kronecker Products
Registro en:
Ieee Signal Processing Letters. Ieee-inst Electrical Electronics Engineers Inc, v. 24, p. 559 - 563, 2017.
1070-9908
1558-2361
WOS:000398855800008
10.1109/LSP.2017.2681159
Autor
Dantas
Cassio Fraga; da Costa
Michele N.; Lopes
Renato da Rocha
Institución
Resumen
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) 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 that 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 subdictionaries. This approach, named sum of Kronecker products, is demonstrated experimentally in an image denoising application. 24 5 559 563 FAPESP [2014/23936-4] CAPES Agency Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)