# Gaussian mixture model

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## 노트

### 위키데이터

- ID : Q20025160

### 말뭉치

- The BayesianGaussianMixture object implements a variant of the Gaussian mixture model with variational inference algorithms.
^{[1]} - A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities.
^{[2]} - 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.
^{[2]} - Now we attempt the same strategy for deriving the MLE of the Gaussian mixture model.
^{[3]} - So how does GMM use the concept of EM and how can we apply it for a given set of points?
^{[4]} - Thus, we arrive at the terms Gaussian mixture models (GMMs) and mixtures of Gaussians.
^{[5]} - Unfortunately, the GMM approach fails when the background has very high frequency variations.
^{[5]} - (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).
^{[5]} - A Bayesian Gaussian mixture model is commonly extended to fit a vector of unknown parameters (denoted in bold), or multivariate normal distributions.
^{[6]} - 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.
^{[6]} - Probabilistic mixture models such as Gaussian mixture models (GMM) are used to resolve point set registration problems in image processing and computer vision fields.
^{[6]} - The EM algorithm for a univariate Gaussian mixture model with K K K components is described below.
^{[7]} - Each distribution is called a mode of the GMM and represents a cluster of data points.
^{[8]} - In computer vision applications, GMM are often used to model dictionaries of visual words.
^{[8]} - For this reason, it is sometimes desirable to globally decorrelated the data before learning a GMM mode.
^{[8]} - Alternatively, a user can specifiy manually the initial paramters of the GMM model by using the custom initalization method.
^{[8]} - We proposed GMM-based approaches to classify features and estimate the number of clusters in a data-driven way.
^{[9]} - 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.
^{[9]} - Using the peak positions as new Gaussian centers, we recalculated the GMM and defined the cluster regions based on the new Gaussian distributions.
^{[9]} - Of note, in our GMM-based framework, merging of clusters is currently done manually using the GUI we developed (Supplementary Fig.
^{[9]} - In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters.
^{[10]} - To be specific, the DE is employed to initialize the GMM parameters.
^{[10]} - 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.
^{[10]} - The EM algorithm is utilized to estimate the GMM parameter set.
^{[10]}

### 소스

- ↑ 2.1. Gaussian mixture models — scikit-learn 0.23.2 documentation
- ↑
^{2.0}^{2.1}Gaussian Mixture Models - ↑ Introduction to EM: Gaussian Mixture Models
- ↑ Clustering Algorithm Python
- ↑
^{5.0}^{5.1}^{5.2}Gaussian Mixture Model - an overview - ↑
^{6.0}^{6.1}^{6.2}Mixture model - ↑ Gaussian Mixture Model
- ↑
^{8.0}^{8.1}^{8.2}^{8.3}Tutorials > Gaussian Mixture Models - ↑
^{9.0}^{9.1}^{9.2}^{9.3}Spike sorting with Gaussian mixture models - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

## 메타데이터

### 위키데이터

- ID : Q20025160