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Informatics in Control, Automation and Robotics. ICINCO 2017.
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SYSNO ASEP 0504124 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data Author(s) Suzdaleva, Evženie (UTIA-B) ORCID
Nagy, Ivan (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Informatics in Control, Automation and Robotics. ICINCO 2017.. - Cham : Springer, 2020 / Gusikhin O. ; Madani K. - ISBN 978-3-030-11292-9 Pages s. 679-698 Number of pages 19 s. Number of pages 812 Publication form Print - P Language eng - English Country CH - Switzerland Keywords Mixture-based clustering ; Recursive mixture estimation ; Different components ; Non-negative data ; Bayesian estimation Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability R&D Projects GA15-03564S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000493283300034 EID SCOPUS 85065472276 DOI 10.1007/978-3-030-11292-9_34 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021
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