Luca Scrucca

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Mixture-based estimation of entropy

The entropy is a measure of uncertainty that plays a central role in inf...
10/08/2020 ∙ by Stéphane Robin, et al. ∙ 0 ∙ share

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Handling missing data in model-based clustering

Gaussian Mixture models (GMMs) are a powerful tool for clustering, class...
06/04/2020 ∙ by Alessio Serafini, et al. ∙ 0 ∙ share

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A fast and efficient Modal EM algorithm for Gaussian mixtures

In the modal approach to clustering, clusters are defined as the local m...
02/10/2020 ∙ by Luca Scrucca, et al. ∙ 0 ∙ share

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Projection pursuit based on Gaussian mixtures and evolutionary algorithms

We propose a projection pursuit (PP) algorithm based on Gaussian mixture...
12/27/2019 ∙ by Luca Scrucca, et al. ∙ 0 ∙ share

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How bettering the best? Answers via blending models and cluster formulations in density-based clustering

With the recent growth in data availability and complexity, and the asso...
11/15/2019 ∙ by Alessandro Casa, et al. ∙ 0 ∙ share

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A transformation-based approach to Gaussian mixture density estimation for bounded data

Finite mixture of Gaussian distributions provide a flexible semi-paramet...
07/17/2018 ∙ by Luca Scrucca, et al. ∙ 0 ∙ share

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Model-based Clustering with Sparse Covariance Matrices

Finite Gaussian mixture models are widely used for model-based clusterin...
11/21/2017 ∙ by Michael Fop, et al. ∙ 0 ∙ share

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Model-based SIR for dimension reduction

A new dimension reduction method based on Gaussian finite mixtures is pr...
08/10/2015 ∙ by Luca Scrucca, et al. ∙ 0 ∙ share

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Dimension reduction for model-based clustering

We introduce a dimension reduction method for visualizing the clustering...
08/07/2015 ∙ by Luca Scrucca, et al. ∙ 0 ∙ share

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Luca Scrucca

Featured Co-authors

Thomas Brendan Murphy

Stéphane Robin

Giovanna Menardi

Alessandro Casa

Michael Fop

Alessio Serafini

Mixture-based estimation of entropy

Handling missing data in model-based clustering

A fast and efficient Modal EM algorithm for Gaussian mixtures

Projection pursuit based on Gaussian mixtures and evolutionary algorithms

How bettering the best? Answers via blending models and cluster formulations in density-based clustering

A transformation-based approach to Gaussian mixture density estimation for bounded data

Model-based Clustering with Sparse Covariance Matrices

Model-based SIR for dimension reduction

Dimension reduction for model-based clustering