Gaussian Mixture Model Selection Using Multiple Random Subsampling with Initialization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926662" target="_blank" >RIV/49777513:23520/15:43926662 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-23192-1_57" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-23192-1_57</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-23192-1_57" target="_blank" >10.1007/978-3-319-23192-1_57</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gaussian Mixture Model Selection Using Multiple Random Subsampling with Initialization

Popis výsledku v původním jazyce

Selecting optimal number of components in Gaussian Mixture Model (GMM) is of interest to many researchers in the last few decades. Most current approaches are based on information criterion, which was introduced by Akaike (1974) and modified by many other researchers. The standard approach uses the EM algorithm, which fits model parameters to training data and determines log-likelihood functions for increasing number of components. Penalized forms of log-likelihood function are then used for selecting number of components. Just searching for new or modified forms of penalty function is subject of permanent effort how to improve or do robust these methods for various type of distributed data. Our new technique for selection of optimal number of GMM components is based on Multiple Random Subsampling of training data with Initialization of the EM algorithm (MuRSI). Results of many performed experiments demonstrate the advantages of this method.

Název v anglickém jazyce

Gaussian Mixture Model Selection Using Multiple Random Subsampling with Initialization

Popis výsledku anglicky

Selecting optimal number of components in Gaussian Mixture Model (GMM) is of interest to many researchers in the last few decades. Most current approaches are based on information criterion, which was introduced by Akaike (1974) and modified by many other researchers. The standard approach uses the EM algorithm, which fits model parameters to training data and determines log-likelihood functions for increasing number of components. Penalized forms of log-likelihood function are then used for selecting number of components. Just searching for new or modified forms of penalty function is subject of permanent effort how to improve or do robust these methods for various type of distributed data. Our new technique for selection of optimal number of GMM components is based on Multiple Random Subsampling of training data with Initialization of the EM algorithm (MuRSI). Results of many performed experiments demonstrate the advantages of this method.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computer Analysis of Images and Patterns, 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015, Proceedings, Part I

ISBN

978-3-319-23191-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

678-689

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Valletta, Malta

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000364705500057