JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Self-Organizing Network for Normal Mixtures
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Self-Organizing Network for Normal Mixtures
Ahn, Sung-Mahn; Kim, Myeong-Kyun;
  PDF(new window)
 Abstract
A self-organizing network is designed to estimate parameters of normal mixtures. SOMN achieves fast convergence and low possibility of divergence even when sample sizes are small, while PMLE eliminate unnecessary components. The proposed network effectively combines the good properties of SOMN and PMLE. Simulation verifies that the proposed network eliminates unnecessary components in normal mixtures when sample sizes are relatively small.
 Keywords
Self-organizing network;normal mixtures;EM algorithm;
 Language
Korean
 Cited by
1.
병렬처리를 통한 정규혼합분포의 추정,이철희;안성만;

Communications for Statistical Applications and Methods, 2012. vol.19. 3, pp.459-469 crossref(new window)
1.
Parallel Implementations of the Self-Organizing Network for Normal Mixtures, Communications for Statistical Applications and Methods, 2012, 19, 3, 459  crossref(new windwow)
 References
1.
Ahn, S. and Baik, S. W. (2011). Estimating parameters in multivariate normal mixtures, The Korean Communications in Statistics, 18, 357-366. crossref(new window)

2.
Ciuperca, G., Ridolfi, A. and Idier, J. (2003). Penalized maximum likelihood estimator for normal mixtures, Scandinavian Journal of Statistics, 30, 45-59. crossref(new window)

3.
Dempster, A., Laird, N. and Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society: Series B, 39, 1-38.

4.
Hathaway, R. J. (1985). A constrained formulation of maximum likelihood estimation for normal mixture distributions, Annals of Statistics, 13, 795-800. crossref(new window)

5.
Kullback, S. and Leibler, R. A. (1951). On information and sufficiency, The Annals of Mathematical Statistics, 22, 79-86. crossref(new window)

6.
Redner, R. A. and Walker, H. F. (1984). Mixture densities, maximum Likelihood and the EM algorithm, SIAM Review, 26, 195-239. crossref(new window)

7.
Robbins, H. and Monro, S. (1951). A stochastic approximation method, The Annals of Mathematical Statistics, 22, 400-407. crossref(new window)

8.
Titterington, D. M. (1984). Recursive parameter estimation using incomplete data, Journal of the Royal Statistical Society: Series B, 46, 257-267.

9.
Yin, H. and Allinson, N. (2001a). Self-organizing mixture networks for probability density estimation, IEEE Transactions on Neural Networks, 12, 405-411. crossref(new window)

10.
Yin, H. and Allinson, N. (2001b). Bayesian self-organizing map for Gaussian mixtures, IEE Proceedings - Vision, Image, and Signal Processing, 234-240.

11.
Xu, L. and Jordan, M. I. (1996). On convergence properties of the EM algorithm for Gaussian mixtures, Neural Computation, 8, 129-151. crossref(new window)