0427607 20240103204149.420140519235959.9 000334676700007 10.1109/MSP.2014.2298045 10 s. P cav_un_epca*02532131053-5888313 (2014)34-43 joint matrices decomposition tensor decomposition blind source separation cav_un_auth*0302679 Chabriel G. FR 10 cav_un_auth*0302680 Kleinsteuber M. DE 10 cav_un_auth*0302681 Moreau E. FR 10 cav_un_auth*0302682 Shen H. DE 10 cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics 50 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0213973 Yeredor A. IL 10 http://library.utia.cas.cz/separaty/2014/SI/tichavsky-0427607.pdf GA102/09/1278 GA ČR cav_un_auth*0253174 This article provides a comprehensive survey of matrix joint decomposition techniques in the context of source separation. More precisely, we first intend to elaborate upon the signal models leading to different useful sets of matrices and their joint decompositions. Second, we present recent identifiability results and algorithms in distinguishing important classes. 2015 BB 6 RVO:67985556 http://hdl.handle.net/11104/0233600 S ENGINEERINGELECTRICALELECTRONIC 2.891 1.710 Q1 99.13 98.594 Q1* Q1* 2014 000334676700007 WOS cav_un_epca*0253213 IEEE Signal Processing Magazine 1053-5888 1558-0792 Roč. 31 č. 3 2014 34 43