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Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm
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SYSNO ASEP 0484891 Druh ASEP J - Článek v odborném periodiku Zařazení RIV Záznam nebyl označen do RIV Poddruh J Ostatní články Název Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm Tvůrce(i) Štepánová, K. (CZ)
Vavrečka, Michal (UIVT-O)Zdroj.dok. Pattern Analysis and Applications - ISSN 1433-7541
Roč. 21, č. 1 (2018), s. 181-192Poč.str. 12 s. Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova Clustering ; EM algorithm ; Gaussian mixture model ; Mixture model ; Number of clusters DOI 10.1007/s10044-016-0576-5 Anotace The brain must deal with a massive flow of sensory information without receiving any prior information. Therefore, when creating cognitive models, it is important to acquire as much information as possible from the data itself. Moreover, the brain has to deal with an unknown number of components (concepts) contained in a dataset without any prior knowledge. Most of the algorithms in use today are not able to effectively copy this strategy. We propose a novel approach based on neural modelling fields theory (NMF) to overcome this problem. The algorithm combines NMF and greedy Gaussian mixture models. The novelty lies in the combination of information criterion with the merging algorithm. The performance of the algorithm was compared with other well-known algorithms and tested both on artificial and real-world datasets. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2018
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