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gptkbp:instanceOf
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gptkb:Machine_learning_algorithm
gptkb:statistical_analysis
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gptkbp:alternativeTo
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gptkb:Dirichlet_process_mixture_models
gptkb:Finite_mixture_models
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gptkbp:application
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gptkb:Bioinformatics
Speech recognition
Image segmentation
Anomaly detection
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gptkbp:assumes
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Independence between components
Data is generated from a mixture of several Gaussian distributions
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gptkbp:basedOn
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gptkb:Gaussian_distribution
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gptkbp:canBe
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Diagonal covariance
Full covariance
Multivariate
Spherical covariance
Univariate
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gptkbp:canBeRegularizedWith
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Covariance regularization
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gptkbp:canBeTrainedWith
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Bayesian inference
Maximum likelihood estimation
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gptkbp:category
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gptkb:Probabilistic_model
Generative model
Unsupervised learning
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gptkbp:component
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Covariance matrix
Mean vector
Mixing coefficient
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gptkbp:extendsTo
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gptkb:Variational_inference
gptkb:Mixture_of_experts
gptkb:Mixture_of_factor_analyzers
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gptkbp:implementedIn
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gptkb:MATLAB
gptkb:TensorFlow_Probability
gptkb:scikit-learn
R
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gptkbp:introduced
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gptkb:Karl_Pearson
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gptkbp:introducedIn
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1894
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gptkbp:limitation
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Assumes Gaussianity
Can overfit with too many components
Sensitive to initialization
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gptkbp:modelSelectionMethod
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gptkb:Akaike_Information_Criterion
gptkb:Bayesian_Information_Criterion
|
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gptkbp:output
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Cluster assignments
Posterior probabilities
Probability densities
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gptkbp:parameterEstimationMethod
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gptkb:Expectation-Maximization_algorithm
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gptkbp:relatedTo
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gptkb:K-means_clustering
Hidden Markov models
Latent variable models
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gptkbp:usedIn
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Clustering
Density estimation
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gptkbp:visualizes
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Ellipses in 2D
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gptkbp:bfsParent
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gptkb:Machine_Learning_by_Andrew_Ng
gptkb:Scikit-learn
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gptkbp:bfsLayer
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6
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https://www.w3.org/2000/01/rdf-schema#label
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Gaussian mixture models
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