4 citations · 5 across the 2 of their papers we have counts for
2 papers
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★ 4 cited
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…