In the class of mixtures with random covariates, the generalized linear Gaussian cluster-weighted model (GLGCWM) has been recently proposed; in each mixture component, it models the response variable within the exponential family of distributions and the vector of real-valued covariates according to the multivariate Gaussian distribution. Due to the number of free parameters of each covariance matrix of the component Gaussian distributions, a family of fourteen parsimonious GLGCWMs is here introduced by applying some constraints on the eigen decomposition of these matrices. This novel family of models is also applied to a real data set where it gives good classification performance, especially when compared with more established mixture-based approaches.

Parsimony in Mixtures with Random Covariates

INGRASSIA, Salvatore;
2013-01-01

Abstract

In the class of mixtures with random covariates, the generalized linear Gaussian cluster-weighted model (GLGCWM) has been recently proposed; in each mixture component, it models the response variable within the exponential family of distributions and the vector of real-valued covariates according to the multivariate Gaussian distribution. Due to the number of free parameters of each covariance matrix of the component Gaussian distributions, a family of fourteen parsimonious GLGCWMs is here introduced by applying some constraints on the eigen decomposition of these matrices. This novel family of models is also applied to a real data set where it gives good classification performance, especially when compared with more established mixture-based approaches.
2013
9788867871179
Cluster-Weighted Models; Model-based clustering; generalized linear models; eigen decomposition; parsimonious mixtures
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/68852
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