Comparison of Various Definitions of Proximity in Mixture Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00461565" target="_blank" >RIV/67985556:_____/16:00461565 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0005982805270534" target="_blank" >http://dx.doi.org/10.5220/0005982805270534</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0005982805270534" target="_blank" >10.5220/0005982805270534</a>

Result language
angličtina
Original language name
Comparison of Various Definitions of Proximity in Mixture Estimation
Original language description
Classification is one of the frequently demanded tasks in data analysis. There exists a series of approaches in this area. This paper is oriented towards classification using the mixture model estimation, which is based on detection of density clusters in the data space and fitting the component models to them. A chosen function of proximity of the actually measured data to individual mixture components and the component shape play a significant role in solving the mixture-based classification task. This paper considers definitions of the proximity for several types of distributions describing the mixture components and compares their properties with respect to speed and quality of the resulting estimation interpreted as a classification task. Normal, exponential and uniform distributions as the most important models used for describing both Gaussian and non-Gaussian data are considered. Illustrative experiments with results of the comparison are provided.
Czech name
—
Czech description
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Project
<a href="/en/project/GA15-03564S" target="_blank" >GA15-03564S: Clustering and classification using recursive mixture estimation</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016)
ISBN
978-989-758-198-4
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
527-534
Publisher name
SCITEPRESS
Place of publication
Setubal
Event location
Lisbon
Event date
Jul 29, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000392610900063

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