The EM Algorithm for Generalized Exponential Mixture Model

2010 ◽  
Author(s):  
Yueyang Teng ◽  
Tie Zhang
Keyword(s):  
Em Algorithm ◽  
Mixture Model ◽  
Exponential Mixture ◽  
The Em Algorithm

scholarly journals Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 373
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Em Algorithm ◽  
Density Estimation ◽  
Mixture Model ◽  
Expectation Maximization ◽  
State Of The Art ◽  
R Package ◽  
Likelihood Estimator ◽  
Local Optima ◽  
The Em Algorithm ◽  
Initialization Algorithm

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

scholarly journals Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yupeng Li ◽  
Jianhua Zhang ◽  
Ruisi He ◽  
Lei Tian ◽  
Hewen Wei
Keyword(s):  
Em Algorithm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Wireless Channel ◽  
Model Parameters ◽  
Em Algorithms ◽  
The Em Algorithm ◽  
Searching Ability

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION

2016 ◽  
Vol 46 (3) ◽  
pp. 779-799 ◽  
Author(s):  
Cuihong Yin ◽  
X. Sheldon Lin
Keyword(s):  
Em Algorithm ◽  
Mixture Model ◽  
Penalty Function ◽  
Real Data ◽  
Efficient Estimation ◽  
Model Parameters ◽  
Simulation Studies ◽  
Distributional Properties ◽  
Data Application ◽  
The Em Algorithm

AbstractThe Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.

Unsupervised Learning Through Generalized Mixture Model

2021 ◽  
Author(s):  
Samyajoy Pal ◽  
Christian Heumann
Keyword(s):  
Cluster Analysis ◽  
Maximum Likelihood ◽  
Em Algorithm ◽  
Unsupervised Learning ◽  
Mixture Models ◽  
Mixture Model ◽  
Learning Problems ◽  
Point Estimate ◽  
Model Parameters ◽  
The Em Algorithm

Abstract A generalized way of building mixture models using different distributions is explored in this article. The EM algorithm is used with some modifications to accommodate different distributions within the same model. The model uses any point estimate available for the respective distributions to estimate the mixture components and model parameters. The study is focused on the application of mixture models in unsupervised learning problems, especially cluster analysis. The convenience of building mixture models using the generalized approach is further emphasised by appropriate examples, exploiting the well-known maximum likelihood and Bayesian estimates of the parameters of the parent distributions.

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