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dc.contributor.author
Tomassi, Diego Rodolfo
dc.contributor.author
Milone, Diego Humberto
dc.contributor.author
Nelson, James D.B.
dc.date.available
2017-05-26T21:22:16Z
dc.date.issued
2015-01
dc.identifier.citation
Tomassi, Diego Rodolfo; Milone, Diego Humberto; Nelson, James D.B.; Wavelet shrinkage using adaptive structured sparsity constraints; Elsevier; Signal Processing; 106; 1-2015; 73-87
dc.identifier.issn
0165-1684
dc.identifier.uri
http://hdl.handle.net/11336/17014
dc.description.abstract
Structured sparsity approaches have recently received much attention in the statistics, machine learning, and signal processing communities. A common strategy is to exploit or assume prior information about structural dependencies inherent in the data; the solution is encouraged to behave as such by the inclusion of an appropriate regularisation term which enforces structured sparsity constraints over sub-groups of data. An important variant of this idea considers the tree-like dependency structures often apparent in wavelet decompositions. However, both the constituent groups and their associated weights in the regularisation term are typically defined a priori. We here introduce an adaptive wavelet denoising framework whereby a sparsity-inducing regulariser is modified based on information extracted from the signal itself. In particular, we use the same wavelet decomposition to detect the location of salient features in the signal, such as jumps or sharp bumps. Given these locations, the weights in the regulariser associated to the groups of coefficients that cover these time locations are modified in order to favour retention of those coefficients. Denoising experiments show that, not only does the adaptive method preserve the salient features better than the non-adaptive constraints, but it also delivers significantly better shrinkage over the signal as a whole.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Structured Sparsity
dc.subject
Regularised Regression
dc.subject
Denoising
dc.subject
Dual-Tree Complex Wavelet Transform
dc.subject.classification
Otras Ciencias de la Computación e Información
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Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Wavelet shrinkage using adaptive structured sparsity constraints
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-05-04T18:44:14Z
dc.journal.volume
106
dc.journal.pagination
73-87
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Tomassi, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
dc.description.fil
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hidricas. Instituto de Investigación En Señales, Sistemas E Inteligencia Computacional; Argentina
dc.description.fil
Fil: Nelson, James D.B.. University College London; Estados Unidos
dc.journal.title
Signal Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.sigpro.2014.07.001
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0165168414002953
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