0396464 20240103203005.320131001235959.9 10.1007/978-3-642-40991-2_35 16 s. P cav_un_epca*0396463978-3-642-40990-50302-9743vol. 8189548-563Berlin HeidelbergSpringer2013 Blind Source Separation Deconvolution Sparsity Scintigraphy 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. 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. http://library.utia.cas.cz/separaty/2013/AS/tichy-sparsity in bayesian blind source separation and deconvolution.pdf GA13-29225S GA ČR cav_un_auth*0292734 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. cav_un_auth*0294334 The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013) Praha 24.09.2013-26.09.2013 CZ 2014 BB 2 PR http://hdl.handle.net/11104/0224318 100 0.325 16.75 0.51 Q2 Q3 2013 cav_un_epca*0396463 Machine Learning and Knowledge Discovery in Databases 978-3-642-40990-5 0302-9743 548 563 Berlin Heidelberg Springer 2013 Lecture Notes in Computer Science part II vol. 8189