Estimating local optimums in EM algorithm over Gaussian mixture model

EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than th...

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Main Authors: ZHANG, Zhenjie, DAI, Bing Tian, TUNG, Anthony K.H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2008
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Online Access:https://ink.library.smu.edu.sg/sis_research/4166
https://ink.library.smu.edu.sg/context/sis_research/article/5169/viewcontent/196.pdf
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spelling sg-smu-ink.sis_research-51692018-11-22T02:45:27Z Estimating local optimums in EM algorithm over Gaussian mixture model ZHANG, Zhenjie DAI, Bing Tian TUNG, Anthony K.H. EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum. 2008-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4166 info:doi/10.1145/1390156.1390312 https://ink.library.smu.edu.sg/context/sis_research/article/5169/viewcontent/196.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Theory and Algorithms
spellingShingle Databases and Information Systems
Theory and Algorithms
ZHANG, Zhenjie
DAI, Bing Tian
TUNG, Anthony K.H.
Estimating local optimums in EM algorithm over Gaussian mixture model
description EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum.
format text
author ZHANG, Zhenjie
DAI, Bing Tian
TUNG, Anthony K.H.
author_facet ZHANG, Zhenjie
DAI, Bing Tian
TUNG, Anthony K.H.
author_sort ZHANG, Zhenjie
title Estimating local optimums in EM algorithm over Gaussian mixture model
title_short Estimating local optimums in EM algorithm over Gaussian mixture model
title_full Estimating local optimums in EM algorithm over Gaussian mixture model
title_fullStr Estimating local optimums in EM algorithm over Gaussian mixture model
title_full_unstemmed Estimating local optimums in EM algorithm over Gaussian mixture model
title_sort estimating local optimums in em algorithm over gaussian mixture model
publisher Institutional Knowledge at Singapore Management University
publishDate 2008
url https://ink.library.smu.edu.sg/sis_research/4166
https://ink.library.smu.edu.sg/context/sis_research/article/5169/viewcontent/196.pdf
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