izpis_h1_title_alt

Improved initialization of the EM algorithm for mixture model parameter estimation
Panić, Branislav (Avtor), Klemenc, Jernej (Avtor), Nagode, Marko (Avtor)

URLURL - Izvorni URL, za dostop obiščite https://www.mdpi.com/2227-7390/8/3/373/htm# Povezava se odpre v novem oknu

Izvleček
A commonly used tool for estimating the parameters of a mixture model is the Expectation-Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density-estimation datasets and image-segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

Jezik:Angleški jezik
Ključne besede:mixture model, parameter estimation, EM algorithm, REBMIX algorithm, density estimation, clustering, image segmentation
Vrsta gradiva:Članek v reviji (dk_c)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2020
Št. strani:str. 1-29
Številčenje:Vol. 8, iss. 3
UDK:519.254(045)
ISSN pri članku:2227-7390
DOI:10.3390/math8030373 Povezava se odpre v novem oknu
COBISS.SI-ID:17112347 Povezava se odpre v novem oknu
Število ogledov:166
Število prenosov:70
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
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Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Številka projekta:P2-0182
Naslov:Razvojna vrednotenja

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:mešani model, ocena parametrov, EM algoritem, REBMIX algoritem, ocena gostote, porazdelitev verjetnosti, grozdenje, segmentacija slik

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