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...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2008
|
Subjects: | |
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-5169 |
---|---|
record_format |
dspace |
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 |
_version_ |
1770574390558195712 |