Improved initialization of the EM algorithm for mixture model parameter estimation

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

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
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: 29 str.
• Številčenje: Vol. 8, iss. 3, art. 373
• PID: 20.500.12556/RUL-114907
• UDK: 519.254(045)
• ISSN pri članku: 2227-7390
• DOI: 10.3390/math8030373
• COBISS.SI-ID: 17112347
• Datum objave v RUL: 30.03.2020

Gradivo je del revije

• Naslov: Mathematics
• Skrajšan naslov: Mathematics
• Založnik: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 07.03.2020

Sekundarni jezik

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

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: 1000-18-0510