Variable selection in clustering via Dirichlet process mixture models

Variable selection in clustering via Dirichlet process mixture models (2006)

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by Sinae Kim , Mahlet G. Tadesse , Marina Vannucci

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BibTeX

@MISC{Kim06variableselection,
    author = {Sinae Kim and Mahlet G. Tadesse and Marina Vannucci},
    title = { Variable selection in clustering via Dirichlet process mixture models},
    year = {2006}
}

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Abstract

The increased collection of high-dimensional data in various fields has raised a strong interest in clustering algorithms and variable selection procedures. In this paper, we propose a model-based method that addresses the two problems simultaneously. We introduce a latent binary vector to identify discriminating variables and use Dirichlet process mixture models to define the cluster structure. We update the variable selection index using a Metropolis algorithm and obtain inference on the cluster structure via a split-merge Markov chain Monte Carlo technique. We explore the performance of the methodology on simulated data and illustrate an application with a dna microarray study.

Citations

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