Estimating local optimums in EM algorithm over Gaussian mixture model

Author

Zhenjie ZHANG
Bing Tian DAI, Singapore Management UniversityFollow
Anthony K.H. TUNG

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2008

Abstract

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.

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ICML '08: The 25th Annual International Conference on Machine Learning, Helsinki, Finland, July 5-9

First Page

1240

Last Page

1247

ISBN

9781605582054

Identifier

10.1145/1390156.1390312

Publisher

ACM

City or Country

New York

Citation

ZHANG, Zhenjie; DAI, Bing Tian; and TUNG, Anthony K.H.. Estimating local optimums in EM algorithm over Gaussian mixture model. (2008). ICML '08: The 25th Annual International Conference on Machine Learning, Helsinki, Finland, July 5-9. 1240-1247.
Available at: https://ink.library.smu.edu.sg/sis_research/4166

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1145/1390156.1390312