Clustering and classification via cluster-weighted factor analyzers

In model-based clustering and classification, the cluster-weighted model is a convenient approach when the random vector of interest is constituted by a response variable $$Y$$ and by a vector $${\varvec{X}}$$ of $$p$$ covariates. However, its applicability may be limited when $$p$$ is high. To overcome this problem, this paper assumes a latent factor structure for $${\varvec{X}}$$ in each mixture component, under Gaussian assumptions. This …