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Improved initialization of the EM algorithm for mixture model parameter estimation
ID
Panić, Branislav
(
Author
),
ID
Klemenc, Jernej
(
Author
),
ID
Nagode, Marko
(
Author
)
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MD5: A4D1A0E3147081974A5D108768397B79
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https://www.mdpi.com/2227-7390/8/3/373
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Abstract
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.
Language:
English
Keywords:
mixture model
,
parameter estimation
,
EM algorithm
,
REBMIX algorithm
,
density estimation
,
clustering
,
image segmentation
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FS - Faculty of Mechanical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2020
Number of pages:
29 str.
Numbering:
Vol. 8, iss. 3, art. 373
PID:
20.500.12556/RUL-114907
UDC:
519.254(045)
ISSN on article:
2227-7390
DOI:
10.3390/math8030373
COBISS.SI-ID:
17112347
Publication date in RUL:
30.03.2020
Views:
1283
Downloads:
243
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Record is a part of a journal
Title:
Mathematics
Shortened title:
Mathematics
Publisher:
MDPI AG
ISSN:
2227-7390
COBISS.SI-ID:
523267865
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
07.03.2020
Secondary language
Language:
Slovenian
Keywords:
mešani model
,
ocena parametrov
,
EM algoritem
,
REBMIX algoritem
,
ocena gostote
,
porazdelitev verjetnosti
,
grozdenje
,
segmentacija slik
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
1000-18-0510
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