Bayesian Blind Source Separation with Unknown Prior Covariance

0447092 20240103210533.220150910235959.9 84944703922 000363785500041 10.1007/978-3-319-22482-4_41 Bayesian Blind Source Separation with Unknown Prior Covariance 8 s. P cav_un_epca*0447151978-3-319-22481-70302-9743Latent Variable Analysis and Signal Separation352-359ChamSpringer2015VincentE.YeredorA.KoldovskýZ.TichavskýP. Blind source separation Covariance model Variational Bayes approximation Non-negative matrix factorization cav_un_auth*0267768 Tichý Ondřej Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/tichy-0447092.pdf GA13-29225S GA ČR cav_un_auth*0292734 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 the Bayesian 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 the covariance 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. cav_un_auth*0319340 12th International Conference on Latent Variable Analysis and Signal Separation Liberec 25.08.2015-28.08.2015 CZ 2016 BB 2 PO RVO:67985556 http://hdl.handle.net/11104/0249081 S 0.329 Q2 Q2 2015 84944703922 SCOPUS 000363785500041 WOS cav_un_epca*0447151 Latent Variable Analysis and Signal Separation 978-3-319-22481-7 0302-9743 352 359 Cham Springer 2015 Lecture Notes in Computer Science 9237 Vincent E. 340 Yeredor A. 340 Koldovský Z. 340 Tichavský P. 340