Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition
- Autores
- Caiafa, César Federico
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Tensor decomposition models for multidimensional datasets (multiway arrays) have a long history in Mathematics and applied sciences. While these models have recently been applied to multidimensional signal processing, they were developed independently of the theory of sparse representations and Compressed Sensing (CS). We discuss and illustrate recent results revealing connections among tensor decompositions models, recovery of low-rank multidimensional signals and CS theory. It is shown that, if a multidimensional signal has a good low rank or sparse multilinear representation, in the sense of the Tucker decomposition model, then it can be reconstructed from incomplete measurements. We discuss reconstructions methods for the cases where only a subset of fibers (mode-n vectors) in each dimension of the signal are available (Fiber Sampling Tensor Decomposition - FSTD), or when only the values of a limited set of entries are known (Tensor completion or multidimensional inpainting problem) or when measurements are given in a compressed multilinear format (Kronecker CS). We illustrate these methods by computer simulations taken on real world multidimensional signals including Magnetic Resonance Imaging (MRI) datasets and Hyperspectral images of natural scenes.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
New Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013)
Mar del Plata
Argentina
Universidad de Buneos Aires - Materia
-
Tensors
Compressed Sensing
Multidimensional Signals
Tucker decomposition - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/153408
Ver los metadatos del registro completo
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Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor DecompositionCaiafa, César FedericoTensorsCompressed SensingMultidimensional SignalsTucker decompositionhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Tensor decomposition models for multidimensional datasets (multiway arrays) have a long history in Mathematics and applied sciences. While these models have recently been applied to multidimensional signal processing, they were developed independently of the theory of sparse representations and Compressed Sensing (CS). We discuss and illustrate recent results revealing connections among tensor decompositions models, recovery of low-rank multidimensional signals and CS theory. It is shown that, if a multidimensional signal has a good low rank or sparse multilinear representation, in the sense of the Tucker decomposition model, then it can be reconstructed from incomplete measurements. We discuss reconstructions methods for the cases where only a subset of fibers (mode-n vectors) in each dimension of the signal are available (Fiber Sampling Tensor Decomposition - FSTD), or when only the values of a limited set of entries are known (Tensor completion or multidimensional inpainting problem) or when measurements are given in a compressed multilinear format (Kronecker CS). We illustrate these methods by computer simulations taken on real world multidimensional signals including Magnetic Resonance Imaging (MRI) datasets and Hyperspectral images of natural scenes.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaNew Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013)Mar del PlataArgentinaUniversidad de Buneos AiresUniversidad de Buenos AiresAldroubi, AkramCabrelli, Carlos2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectWorkshopBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/153408Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition; New Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013); Mar del Plata; Argentina; 2013; 1-1CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.univie.ac.at/nuhag-php/dateien/talks/Caiafa_2013-04_Abstract.pdfinfo:eu-repo/semantics/altIdentifier/url/https://www.univie.ac.at/nuhag-php/event_NEW/make.php?event=cimpa13Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:01:30Zoai:ri.conicet.gov.ar:11336/153408instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:01:30.819CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
title |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
spellingShingle |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition Caiafa, César Federico Tensors Compressed Sensing Multidimensional Signals Tucker decomposition |
title_short |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
title_full |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
title_fullStr |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
title_full_unstemmed |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
title_sort |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition |
dc.creator.none.fl_str_mv |
Caiafa, César Federico |
author |
Caiafa, César Federico |
author_facet |
Caiafa, César Federico |
author_role |
author |
dc.contributor.none.fl_str_mv |
Aldroubi, Akram Cabrelli, Carlos |
dc.subject.none.fl_str_mv |
Tensors Compressed Sensing Multidimensional Signals Tucker decomposition |
topic |
Tensors Compressed Sensing Multidimensional Signals Tucker decomposition |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Tensor decomposition models for multidimensional datasets (multiway arrays) have a long history in Mathematics and applied sciences. While these models have recently been applied to multidimensional signal processing, they were developed independently of the theory of sparse representations and Compressed Sensing (CS). We discuss and illustrate recent results revealing connections among tensor decompositions models, recovery of low-rank multidimensional signals and CS theory. It is shown that, if a multidimensional signal has a good low rank or sparse multilinear representation, in the sense of the Tucker decomposition model, then it can be reconstructed from incomplete measurements. We discuss reconstructions methods for the cases where only a subset of fibers (mode-n vectors) in each dimension of the signal are available (Fiber Sampling Tensor Decomposition - FSTD), or when only the values of a limited set of entries are known (Tensor completion or multidimensional inpainting problem) or when measurements are given in a compressed multilinear format (Kronecker CS). We illustrate these methods by computer simulations taken on real world multidimensional signals including Magnetic Resonance Imaging (MRI) datasets and Hyperspectral images of natural scenes. Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina New Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013) Mar del Plata Argentina Universidad de Buneos Aires |
description |
Tensor decomposition models for multidimensional datasets (multiway arrays) have a long history in Mathematics and applied sciences. While these models have recently been applied to multidimensional signal processing, they were developed independently of the theory of sparse representations and Compressed Sensing (CS). We discuss and illustrate recent results revealing connections among tensor decompositions models, recovery of low-rank multidimensional signals and CS theory. It is shown that, if a multidimensional signal has a good low rank or sparse multilinear representation, in the sense of the Tucker decomposition model, then it can be reconstructed from incomplete measurements. We discuss reconstructions methods for the cases where only a subset of fibers (mode-n vectors) in each dimension of the signal are available (Fiber Sampling Tensor Decomposition - FSTD), or when only the values of a limited set of entries are known (Tensor completion or multidimensional inpainting problem) or when measurements are given in a compressed multilinear format (Kronecker CS). We illustrate these methods by computer simulations taken on real world multidimensional signals including Magnetic Resonance Imaging (MRI) datasets and Hyperspectral images of natural scenes. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject Workshop Book http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
status_str |
publishedVersion |
format |
conferenceObject |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/153408 Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition; New Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013); Mar del Plata; Argentina; 2013; 1-1 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/153408 |
identifier_str_mv |
Reconstruction of Multiway Arrays from Incomplete Information Using the Tucker Tensor Decomposition; New Trends in Applied Harmonic Analysis Sparse Representations, Compressed Sensing and Multifractal Analysis (CIMPA 2013); Mar del Plata; Argentina; 2013; 1-1 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.univie.ac.at/nuhag-php/dateien/talks/Caiafa_2013-04_Abstract.pdf info:eu-repo/semantics/altIdentifier/url/https://www.univie.ac.at/nuhag-php/event_NEW/make.php?event=cimpa13 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.coverage.none.fl_str_mv |
Internacional |
dc.publisher.none.fl_str_mv |
Universidad de Buenos Aires |
publisher.none.fl_str_mv |
Universidad de Buenos Aires |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |