Accelerating distributed Expectation–Maximization algorithms with frequent updates

Expectation–Maximization (EM) is a popular approach for parameter estimation in many applications, such as image understanding, document classification, and genome data analysis. Despite the popularity of EM algorithms, it is challenging to efficiently implement these algorithms in a distributed env...

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Veröffentlicht in:	Journal of parallel and distributed computing Jg. 111; S. 65 - 75
Hauptverfasser:	Yin, Jiangtao, Zhang, Yanfeng, Gao, Lixin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.01.2018
Schlagworte:	Clustering Concurrent updates Distributed framework Expectation–Maximization Frequent updates Topic modeling Topic modeling Distributed framework Clustering Frequent updates Concurrent updates Expectation–Maximization
ISSN:	0743-7315, 1096-0848
Online-Zugang:	Volltext
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