Expert-based Initialization of Recursive Mixture Estimation
Result description
Initialization is an extremely important part of the mixture estimation process. There exists a series of initialization approaches in the literature concerning the mixture initialization. However, the majority of them is directed at initialization of the expectation-maximization algorithm widely used in this area. This paper focuses on the initialization of the mixture estimation with normal components based on the recursive statistics update of involved distributions, where the mentioned methods are not suitable. Its key part is the choice of the initial statistics. The paper describes several relatively simple initialization techniques primarily based on processing the prior data. The experimental part of the paper represents results of validation on real data.
Keywords
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Expert-based Initialization of Recursive Mixture Estimation
Original language description
Initialization is an extremely important part of the mixture estimation process. There exists a series of initialization approaches in the literature concerning the mixture initialization. However, the majority of them is directed at initialization of the expectation-maximization algorithm widely used in this area. This paper focuses on the initialization of the mixture estimation with normal components based on the recursive statistics update of involved distributions, where the mentioned methods are not suitable. Its key part is the choice of the initial statistics. The paper describes several relatively simple initialization techniques primarily based on processing the prior data. The experimental part of the paper represents results of validation on real data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
GA15-03564S: Clustering and classification using recursive mixture estimation
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2016 IEEE 8th International Conference on Intelligent Systems
ISBN
978-1-5090-1353-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
308-315
Publisher name
IEEE
Place of publication
Sofia
Event location
Sofia
Event date
Sep 4, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391554300044
Basic information
Result type
D - Article in proceedings
CEP
BB - Applied statistics, operational research
Year of implementation
2016