Gaussian mixture model

노트

위키데이터

• ID : Q20025160

말뭉치

1. The BayesianGaussianMixture object implements a variant of the Gaussian mixture model with variational inference algorithms.
2. A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities.
3. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.
4. Now we attempt the same strategy for deriving the MLE of the Gaussian mixture model.
5. So how does GMM use the concept of EM and how can we apply it for a given set of points?
6. Thus, we arrive at the terms Gaussian mixture models (GMMs) and mixtures of Gaussians.
7. Unfortunately, the GMM approach fails when the background has very high frequency variations.
8. (2000) moved away from the parametric approach of the GMM (the latter essentially finds the weights and variances of the component distributions, and thus is parametric).
9. A Bayesian Gaussian mixture model is commonly extended to fit a vector of unknown parameters (denoted in bold), or multivariate normal distributions.
10. A multivariate Gaussian mixture model is used to cluster the feature data into k number of groups where k represents each state of the machine.
11. Probabilistic mixture models such as Gaussian mixture models (GMM) are used to resolve point set registration problems in image processing and computer vision fields.
12. The EM algorithm for a univariate Gaussian mixture model with K K K components is described below.
13. Each distribution is called a mode of the GMM and represents a cluster of data points.
14. In computer vision applications, GMM are often used to model dictionaries of visual words.
15. For this reason, it is sometimes desirable to globally decorrelated the data before learning a GMM mode.
16. Alternatively, a user can specifiy manually the initial paramters of the GMM model by using the custom initalization method.
17. We proposed GMM-based approaches to classify features and estimate the number of clusters in a data-driven way.
18. We first built a GMM of the selected features which overestimated the number of clusters, resulting in a mixture model with more Gaussians than the real number of neurons.
19. Using the peak positions as new Gaussian centers, we recalculated the GMM and defined the cluster regions based on the new Gaussian distributions.
20. Of note, in our GMM-based framework, merging of clusters is currently done manually using the GUI we developed (Supplementary Fig.
21. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters.
22. To be specific, the DE is employed to initialize the GMM parameters.
23. To get a preferable parameter set of the GMM, we embed the EM algorithm in the DE framework and propose a hybrid DE-EM algorithm.
24. The EM algorithm is utilized to estimate the GMM parameter set.