http://iet.metastore.ingenta.com
1887

Robust deterministic annealing based EM algorithm

Robust deterministic annealing based EM algorithm

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A deterministic annealing (DA)-based expectation-maximisation (EM) algorithm is proposed for robust learning of Gaussian mixture models. By combing the DA approach, trimmed likelihood function and Bayesian information criterion (BIC), the proposed algorithm can simultaneously perform model selection and outlier detection, and mitigate the problems of local optima and boundary of parameter space with the conventional EM algorithm. Experiments demonstrate that the proposed algorithm can determine the number of components correctly even though the data are contaminated by outliers.

References

    1. 1)
    2. 2)
    3. 3)
      • Zhao, Q., Miller, D.J.: `A deterministic annealing-based approach for learning and model selection in finite mixture models', Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2004, Montreal, Canada, p. V–457-60.
    4. 4)
    5. 5)
      • M. Grant , S. Boyd . CVX: Matlab software for disciplined convex programming.
    6. 6)
    7. 7)
      • BCI competition II. Available: http://www.bbci.de/competition/ii/.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2011.2797
Loading

Related content

content/journals/10.1049/el.2011.2797
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address