Block-wise Variable Selection for Clustering via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this paper, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

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