0500888 20240103221507.420190201235959.9 85061747380 000458852100008 10.1109/LSP.2019.2897230 5 s. P cav_un_epca*02532121070-9908263 (2019)510-514Institute of Electrical and Electronics Engineers Non-negative matrix factorization Covariance matrix model Blind source separation Variational Bayes method Dynamic renal scintigraphy 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*0371641 Bódiová Lenka Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B 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/2019/AS/tichy-0500888.pdf https://ieeexplore.ieee.org/document/8633424 cav_un_auth*0360229 GA18-07247S GA ČR Non-negative matrix factorization (NMF) is generally an ill-posed problem which requires further regularization. Regularization of NMF using the assumption of sparsity is common as well as regularization using smoothness. In many applications it is natural to assume that both of these assumptions hold together. To avoid ad hoc combination of these assumptions using weighting coefficient, we formulate the problem using a probabilistic model and estimate it in a Bayesian way. Specifically, we use the fact that the assumptions of sparsity and smoothness are different forms of prior covariance matrix modeling. We use a generalized model that includes both sparsity and smoothness as special cases and estimate all its parameters using the variational Bayes method. The resulting matrix factorization algorithm is compared with state-of-the-art algorithms on large clinical dataset of 196 image sequences from dynamic renal scintigraphy. The proposed algorithm outperforms other algorithms in statistical evaluation. WOS BB 20000 20200 20205 2020 3 4 A hod 4ah 20231122143804.5 RVO:67985556 http://hdl.handle.net/11104/0293325 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS S 1 Article Engineering Aerospace|Astronomy Astrophysics|Geosciences Multidisciplinary|Meteorology Atmospheric Sciences C ENGINEERING.ELECTRICAL&ELECTRONIC 3.414 0.482 5.4 0.02646 1.106 11929 167 1.145 25.9 4.55 5579 Q1 2.866 371 Q2 81.438 67.9 Q1 Q4 1.08 Q1 67.857 2019 Soubory v repozitáři: tichy-0500888.pdf 85061747380 SCOPUS PUBMED 000458852100008 WOS cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 26 č. 3 2019 510 514 Institute of Electrical and Electronics Engineers