Informatics in Control, Automation and Robotics. ICINCO 2017.

0504124 - ÚTIA 2021 RIV CH eng M - Monography Chapter
Suzdaleva, Evženie - Nagy, Ivan
Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data.
Informatics in Control, Automation and Robotics. ICINCO 2017.. Cham: Springer, 2020 - (Gusikhin, O.; Madani, K.), s. 679-698. Lecture Notes in Electrical Engineering, 495. ISBN 978-3-030-11292-9
R&D Projects: GA ČR GA15-03564S
Institutional support: RVO:67985556
Keywords : Mixture-based clustering * Recursive mixture estimation * Different components * Non-negative data * Bayesian estimation
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2019/ZS/suzdaleva-0504124.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0295982