It is well-known that the EM algorithm generally converges to a local maximum likelihood estimate. However, there have been many evidences to show that the EM algorithm can converge correctly to the true parameters as long as the overlap of Gaussians in the sample data is small enough. This paper st
Singularity and Slow Convergence of the EM algorithm for Gaussian Mixtures
β Scribed by Hyeyoung Park; Tomoko Ozeki
- Publisher
- Springer US
- Year
- 2009
- Tongue
- English
- Weight
- 660 KB
- Volume
- 29
- Category
- Article
- ISSN
- 1370-4621
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In order to alleviate the problem of local convergence of the usual EM algorithm, a split-and-merge operation is introduced into the EM algorithm for Gaussian mixtures. The split-and-merge equations are ΓΏrst presented theoretically. These equations show that the merge operation is a well-posed probl
The Normal-Inverse Gaussian distribution arises as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution. This article deals with Maximum Likelihood estimation of the parameters of the Normal-Inverse Gaussian distribution. Due to the complexity of the likelihood, direct maximiz