Cluster-weighted models representa convenient approach for model-based clustering, especially when the covariates contribute to defining the cluster-structure of the data. However, applicability may be limited when the number of covariates is high and performance may be affected by noise and outliers. To overcome these problems, common/uncommon t-factor analyzers for the covariates, and a $t$-distribution for the response variable, are here assumed in each mixture component. A family of twenty parsimonious variants of this model is also presented and the alternatingexpectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family, is described.Artificial and real data show that these models have very good clustering performance and that the algorithm is able to recover the parameters very well.

Cluster-Weighted t-Factor Analyzers for Robust Model-Based Clustering and Dimension Reduction

Punzo, A.;Ingrassia, S.;
2015

Abstract

Cluster-weighted models representa convenient approach for model-based clustering, especially when the covariates contribute to defining the cluster-structure of the data. However, applicability may be limited when the number of covariates is high and performance may be affected by noise and outliers. To overcome these problems, common/uncommon t-factor analyzers for the covariates, and a $t$-distribution for the response variable, are here assumed in each mixture component. A family of twenty parsimonious variants of this model is also presented and the alternatingexpectation-conditional maximization algorithm, for maximum likelihood estimation of the parameters of all models in the family, is described.Artificial and real data show that these models have very good clustering performance and that the algorithm is able to recover the parameters very well.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/33524
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