Comparison of Various Definitions of Proximity in Mixture Estimation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Various Definitions of Proximity in Mixture Estimation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of Various Definitions of Proximity in Mixture Estimation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

<a href="/cs/project/GA15-03564S" target="_blank" >GA15-03564S: Shlukování a klasifikace s využitím rekurzivního odhadování modelu směsi distribucí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016)

ISBN

978-989-758-198-4

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

527-534

Název nakladatele

SCITEPRESS

Místo vydání

Setubal

Místo konání akce

Lisbon

Datum konání akce

29. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000392610900063