0504124 20240111141018.420190423235959.9 85065472276 000493283300034 10.1007/978-3-030-11292-9_34 19 s. 812 P cav_un_epca*0504123978-3-030-11292-9495679-698ChamSpringer2020GusikhinO.MadaniK. Mixture-based clustering Recursive mixture estimation Different components Non-negative data Bayesian estimation cav_un_auth*0355927 Suzdaleva Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2019/ZS/suzdaleva-0504124.pdf GA15-03564S GA ČR cav_un_auth*0321440 The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view. BB 10000 10100 10103 2021 2 RVO:67985556 http://hdl.handle.net/11104/0295982 S 3+4 Proceedings Paper Automation Control Systems|Computer Science Theory Methods|Engineering Electrical Electronic|Robotics 2020 85065472276 SCOPUS PUBMED 000493283300034 WOS pdf cav_un_epca*0504123 Informatics in Control, Automation and Robotics. ICINCO 2017. 978-3-030-11292-9 679 698 Cham Springer 2020 Lecture Notes in Electrical Engineering 495 340 Gusikhin O. 340 Madani K.