H. Inouzhe Valdés, E. del Barrio, C. Matrán Bea
Cluster analysis addresses the detection of data grouping in data sets. Within this, too vague, description, model-based clustering aims to find particularly shaped groupings -clusters- according to specified distributions. In this setting, the clusters provided by the method are described by probability (often Gaussian) distributions, that can be considered as elements of an abstract space. Particular interest has been deserved by the L2 Wasserstein distance, leading to a rich set-up for developing statistical concepts in a parallel way to those known on Euclidean spaces. This is the case of the k-barycenters, the abstract version of k-means, by large the widest used method in clustering problems, recently introduced in the Wasserstein space even in a robust version. We focus on the application of the (trimmed) Wasserstein k-barycenters to some of the fundamental problems present in cluster analysis. This includes parallelization or stabilization of procedures ...
Palabras clave / Keywords: cluster analysis, k-means, optimal transportation, cluster aggregation
Programado
Sesión invitada SI08 Métodos Estadísticos Robustos y Aplicaciones II (Organizadores: Luis Ángel García Escudero y Agustín Mayo Íscar)
1 de junio de 2018 16:00
Sala Cristal
