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Found 5 papers, 0 papers with code
Handling missing data in model-based clustering
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data.
A fast and efficient Modal EM algorithm for Gaussian mixtures
In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametrically or using finite mixture models.
Projection pursuit based on Gaussian mixtures and evolutionary algorithms
We show that this semi-parametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualised.
Model-based SIR for dimension reduction
A new dimension reduction method based on Gaussian finite mixtures is proposed as an extension to sliced inverse regression (SIR).
Dimension reduction for model-based clustering
Information on the dimension reduction subspace is obtained from the variation on group means and, depending on the estimated mixture model, on the variation on group covariances.
