Variable selection in clustering via Dirichlet process mixture models

• 成果类型: Article
• 署名作者: Kim, Sinae; Tadesse, Mahlet G.; Vannucci, Marina
• 署名单位: Texas A&M University System; Texas A&M University College Station; University of Pennsylvania; Texas A&M University System; Texas A&M University College Station
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.4.877
• 发表日期: 2006
• 页码: 877893
• 关键词: bayesian-analysis; sampling methods; unknown number; CLASSIFICATION; distributions; inference

摘要

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.