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Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis

contributor authorVervliet, Nico
contributor authorDebals, Otto
contributor authorSorber, Laurent
contributor authorDe Lathauwer, Lieven
date accessioned2020-03-13T00:18:59Z
date available2020-03-13T00:18:59Z
date issued2014
identifier issn1053-5888
identifier other6879619.pdf
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/1142219?show=full
formatgeneral
languageEnglish
publisherIEEE
titleBreaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis
typeJournal Paper
contenttypeMetadata Only
identifier padid8324723
subject keywordsBig Data
subject keywordsscientific information systems
subject keywordstensors
subject keywordsBig Data analysis
subject keywordsCS methods
subject keywordscompressed sensing
subject keywordscomputational requirements
subject keywordscurse of dimensionality
subject keywordsdata sets
subject keywordsexponential dependency
subject keywordshigher-order tensors
subject keywordsincomplete tensors decompositions
subject keywordsmemory requirements
subject keywordsmultilinear models
subject keywordstensor problems
subject keywordstensor-based scientific computing
subject keywordsApproximation algorithms
subject keywordsApproximation methods
subject keywordsBig data
subject keywordsData storage
subject keywordsMatrix decomposition
subject keywordsScientific computing
subject keywordsSignal processing algorithms
subject keywordsTensile stres
identifier doi10.1109/MSP.2014.2329429
journal titleSignal Processing Magazine, IEEE
journal volume31
journal issue5
filesize1818020
citations0


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