Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
Machine learning algorithm |
| gptkbp:application |
Speech recognition
Pattern recognition Image segmentation Anomaly detection |
| gptkbp:assumes |
Data points are generated from a mixture of Gaussians
|
| gptkbp:basedOn |
gptkb:Gaussian_distribution
|
| gptkbp:canBe |
Outlier detection
Dimensionality reduction Multivariate Univariate Data generation Missing data imputation Soft clustering |
| gptkbp:canBeRegularizedBy |
Bayesian priors
|
| gptkbp:component |
Gaussian component
|
| gptkbp:distinctFrom |
K-means does not model covariance
|
| gptkbp:extendsTo |
gptkb:Hidden_Markov_Models
Dirichlet Process Gaussian Mixture Models |
| gptkbp:hasModel |
Multimodal distributions
|
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian Mixture Models
|
| gptkbp:implementedIn |
gptkb:TensorFlow
gptkb:MATLAB gptkb:scikit-learn R |
| gptkbp:introduced |
gptkb:Karl_Pearson
|
| gptkbp:introducedIn |
1894
|
| gptkbp:limitation |
Sensitive to initialization
May converge to local optima Assumes Gaussianity of components |
| gptkbp:output |
Cluster assignments
Probability estimates |
| gptkbp:parameter |
Mean
Mixing coefficient Covariance |
| gptkbp:parameterEstimationMethod |
gptkb:Expectation-Maximization_algorithm
|
| gptkbp:relatedTo |
gptkb:Expectation-Maximization_algorithm
gptkb:K-means_clustering Latent variable models Unsupervised learning Cluster analysis Mixture models Probabilistic models |
| gptkbp:requires |
Number of components specified
|
| gptkbp:usedFor |
Clustering
Density estimation |
| gptkbp:visualizes |
Ellipses in 2D
|
| gptkbp:bfsParent |
gptkb:Unsupervised_Learning
gptkb:Kaldi_model |
| gptkbp:bfsLayer |
7
|