|Table of Contents|

[1] Zeng Hong, Lu Wei, Song Aiguo,. Gaussian mixture model clusteringwith completed likelihood minimum message length criterion [J]. Journal of Southeast University (English Edition), 2013, 29 (1): 43-47. [doi:10.3969/j.issn.1003-7985.2013.01.009]
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Gaussian mixture model clusteringwith completed likelihood minimum message length criterion()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
29
Issue:
2013 1
Page:
43-47
Research Field:
Automation
Publishing date:
2013-03-20

Info

Title:
Gaussian mixture model clusteringwith completed likelihood minimum message length criterion
Author(s):
Zeng Hong1 Lu Wei2 Song Aiguo1
1 School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2 College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Keywords:
Gaussian mixture model non-Gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion
PACS:
TP181
DOI:
10.3969/j.issn.1003-7985.2013.01.009
Abstract:
An improved Gaussian mixture model(GMM)-based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation-maximization(EM)algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

References:

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Memo

Memo:
Biography: Zeng Hong(1981—), male, doctor, lecturer, hzeng@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61105048, 60972165), the Doctoral Fund of Ministry of Education of China(No.20110092120034), the Natural Science Foundation of Jiangsu Province(No.BK2010240), the Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Human Resources and Social Security of China(No.6722000008), and the Open Fund of Jiangsu Province Key Laboratory for Remote Measuring and Control(No.YCCK201005).
Citation: Zeng Hong, Lu Wei, Song Aiguo.Gaussian mixture model clustering with completed likelihood minimum message length criterion[J].Journal of Southeast University(English Edition), 2013, 29(1):43-47.[doi:10.3969/j.issn.1003-7985.2013.01.009]
Last Update: 2013-03-20