Bayesian Blind Source Separation with Unknown Prior Covariance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F15%3A00231473" target="_blank" >RIV/68407700:21340/15:00231473 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/15:00447092
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-22482-4_41" target="_blank" >http://dx.doi.org/10.1007/978-3-319-22482-4_41</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-22482-4_41" target="_blank" >10.1007/978-3-319-22482-4_41</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian Blind Source Separation with Unknown Prior Covariance
Original language description
The task of blind source separation (BSS) is to recover original signal sources which are observed only via their superposition with unknown weights. Since we are interested in estimation of the number of relevant sources in noisy observation, we use theBayesian formulation which automatically removes spurious sources. A tool for this behavior is joint estimation of the unknown prior covariance matrix of the sources in tandem with the sources. In this work, we study the effect of various choices of thecovariance matrix structure. Specifically, we compare models using the automatic relevance determination (ARD) principle on the first and the second diagonal, as well as full covariance matrix with Wishart prior. We obtain five versions of the variational BSS algorithm. These are tested on synthetic data and on a selected dataset from dynamic renal scintigraphy. MATLAB implementation of the methods is available for download.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Latent Variable Analysis and Signal Separation
ISBN
978-3-319-22481-7
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
352-359
Publisher name
Springer
Place of publication
Cham
Event location
Liberec
Event date
Aug 25, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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