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Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters

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  • Benhuai Xie
  • Wei Pan
  • Xiaotong Shen

Abstract

Summary Penalized model‐based clustering has been proposed for high‐dimensional but small sample‐sized data, such as arising from genomic studies; in particular, it can be used for variable selection. A new regularization scheme is proposed to group together multiple parameters of the same variable across clusters, which is shown both analytically and numerically to be more effective than the conventional L1 penalty for variable selection. In addition, we develop a strategy to combine this grouping scheme with grouping structured variables. Simulation studies and applications to microarray gene expression data for cancer subtype discovery demonstrate the advantage of the new proposal over several existing approaches.

Suggested Citation

  • Benhuai Xie & Wei Pan & Xiaotong Shen, 2008. "Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters," Biometrics, The International Biometric Society, vol. 64(3), pages 921-930, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:921-930
    DOI: 10.1111/j.1541-0420.2007.00955.x
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    References listed on IDEAS

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    Cited by:

    1. Thierry Chekouo & Alejandro Murua, 2018. "High-dimensional variable selection with the plaid mixture model for clustering," Computational Statistics, Springer, vol. 33(3), pages 1475-1496, September.
    2. Yan Li & Chun Yu & Yize Zhao & Weixin Yao & Robert H. Aseltine & Kun Chen, 2022. "Pursuing sources of heterogeneity in modeling clustered population," Biometrics, The International Biometric Society, vol. 78(2), pages 716-729, June.

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