Branislav Panić (Avtor), Jernej Klemenc (Avtor), Marko Nagode (Avtor)

Povzetek

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.

Ključne besede

mešani model;ocena parametrov;EM algoritem;REBMIX algoritem;ocena gostote;porazdelitev verjetnosti;grozdenje;segmentacija slik;mixture model;parameter estimation;EM algorithm;REBMIX algorithm;density estimation;clustering;image segmentation;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 519.254(045)
COBISS: 17112347 Povezava se bo odprla v novem oknu
ISSN: 2227-7390
Matična publikacija: Mathematics
Št. ogledov: 523
Št. prenosov: 162
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: mešani model;ocena parametrov;EM algoritem;REBMIX algoritem;ocena gostote;porazdelitev verjetnosti;grozdenje;segmentacija slik;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-29
Letnik: ǂVol. ǂ8
Zvezek: ǂiss. ǂ3
Čas izdaje: 2020
DOI: 10.3390/math8030373
ID: 11476499