About: Sparsity in Bayesian Blind Source Separation and Deconvolution

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution, we investigate the use of sparsity criteria for both the source signal and the convolution kernels. A probabilistic model of the problem is introduced and its Variational Bayesian solution derived. The sparsity of the solution is achieved by introduction of unknown variance of the prior on all elements of the convolution kernels and the mixing matrix. Properties of the model are analyzed on simulated data and compared with state of the art methods. Performance of the algorithm is demonstrated on the problem of decomposition of a sequence of medical data. Specifically, the assumption of sparseness is shown to suppress artifacts of unconstrained separation method. Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution, we investigate the use of sparsity criteria for both the source signal and the convolution kernels. A probabilistic model of the problem is introduced and its Variational Bayesian solution derived. The sparsity of the solution is achieved by introduction of unknown variance of the prior on all elements of the convolution kernels and the mixing matrix. Properties of the model are analyzed on simulated data and compared with state of the art methods. Performance of the algorithm is demonstrated on the problem of decomposition of a sequence of medical data. Specifically, the assumption of sparseness is shown to suppress artifacts of unconstrained separation method. (en)
Title	Sparsity in Bayesian Blind Source Separation and Deconvolution Sparsity in Bayesian Blind Source Separation and Deconvolution (en)
skos:prefLabel	Sparsity in Bayesian Blind Source Separation and Deconvolution Sparsity in Bayesian Blind Source Separation and Deconvolution (en)
skos:notation	RIV/67985556:_____/13:00396464!RIV14-GA0-67985556
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GA13-29225S)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Šmídl, Václav Tichý, Ondřej
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	106778
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/13:00396464
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Blind Source Separation; Deconvolution; Sparsity; Scintigraphy (en)
http://linked.open.../riv/klicoveSlovo	Deconvolution Scintigraphy Blind Source Separation Sparsity
http://linked.open...ontrolniKodProRIV	[D7EF3815F95E]
http://linked.open...v/mistoKonaniAkce	Praha
http://linked.open...i/riv/mistoVydani	Berlin Heidelberg
http://linked.open...i/riv/nazevZdroje	Machine Learning and Knowledge Discovery in Databases
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Slepá dekonvoluce obrazu v limitních podmínkách
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Šmídl, Václav Tichý, Ondřej
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-09-24 (xsd:date)
issn	0302-9743
number of pages	16 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-40991-2_35
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-40990-5

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