Initializing the EM algorithm in Gaussian mixture models with an unknown number of components

2012 ◽  
Vol 56 (6) ◽  
pp. 1381-1395 ◽  
Author(s):  
Volodymyr Melnykov ◽  
Igor Melnykov
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Number Of Components ◽  
Unknown Number ◽  
The Em Algorithm

scholarly journals An exact line search scheme to accelerate the EM algorithm: Application to Gaussian mixture models identification

2020 ◽  
Vol 41 ◽  
pp. 101073 ◽  
Author(s):  
Wentao Xiang ◽  
Ahmad Karfoul ◽  
Chunfeng Yang ◽  
Huazhong Shu ◽  
Régine Le Bouquin Jeannès
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Line Search ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Exact Line Search ◽  
The Em Algorithm

scholarly journals Statistical convergence of the EM algorithm on Gaussian mixture models

2020 ◽  
Vol 14 (1) ◽  
pp. 632-660 ◽  
Author(s):  
Ruofei Zhao ◽  
Yuanzhi Li ◽  
Yuekai Sun
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Statistical Convergence ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
The Em Algorithm

scholarly journals Kernel Analysis Based on Dirichlet Processes Mixture Models

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 857
Author(s):  
Jinkai Tian ◽  
Peifeng Yan ◽  
Da Huang
Keyword(s):  
Mixture Models ◽  
Crucial Role ◽  
Gaussian Mixture Models ◽  
Gaussian Process Regression ◽  
Gaussian Mixture ◽  
Spectral Domain ◽  
Dirichlet Processes ◽  
Number Of Components ◽  
Kernel Analysis ◽  
Nonparametric Bayesian Models

Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels. However, the number of components in a GMM is fixed. Thus, this model suffers from overfitting or underfitting. In this paper, we try to combine the spectral domain of kernels with nonparametric Bayesian models. Dirichlet processes mixture models are used to resolve this problem by changing the number of components according to the data size. Multiple experiments have been conducted on this model and it shows competitive performance.

Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges

2016 ◽  
Vol 24 (3) ◽  
pp. e1886 ◽  
Author(s):  
Adam Santos ◽  
Eloi Figueiredo ◽  
Moisés Silva ◽  
Reginaldo Santos ◽  
Claudomiro Sales ◽  
...  
Keyword(s):  
Em Algorithm ◽  
Damage Detection ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture
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