most citedHandling missing data in model-based clustering

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stat.ML2020★ 1 cited

Handling missing data in model-based clustering

Alessio Serafini, Thomas Brendan Murphy, Luca Scrucca

Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missin…

stat.ML2019

Projection pursuit based on Gaussian mixtures and evolutionary algorithms

Luca Scrucca, Alessio Serafini

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP i…