Corpus ID: 16239564

Penalized Model-Based Clustering with Application to Variable Selection

@article{Pan2007PenalizedMC,
  title={Penalized Model-Based Clustering with Application to Variable Selection},
  author={Wei Pan and Xiaotong Shen},
  journal={J. Mach. Learn. Res.},
  year={2007},
  volume={8},
  pages={1145-1164}
}
  • Wei Pan, Xiaotong Shen
  • Published 2007
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • Variable selection in clustering analysis is both challenging and important. In the context of model-based clustering analysis with a common diagonal covariance matrix, which is especially suitable for "high dimension, low sample size" settings, we propose a penalized likelihood approach with an L1 penalty function, automatically realizing variable selection via thresholding and delivering a sparse solution. We derive an EM algorithm to fit our proposed model, and propose a modified BIC as a… CONTINUE READING

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 175 CITATIONS

    Variable selection for model-based high-dimensional clustering and its application to microarray data.

    VIEW 12 EXCERPTS
    CITES BACKGROUND, METHODS & RESULTS
    HIGHLY INFLUENCED

    Pairwise variable selection for high-dimensional model-based clustering.

    VIEW 7 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Penalized model-based clustering with unconstrained covariance matrices.

    VIEW 8 EXCERPTS
    CITES BACKGROUND & METHODS

    Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables.

    VIEW 11 EXCERPTS
    CITES METHODS & BACKGROUND

    FILTER CITATIONS BY YEAR

    2006
    2020

    CITATION STATISTICS

    • 24 Highly Influenced Citations

    • Averaged 19 Citations per year from 2017 through 2019

    • 29% Increase in citations per year in 2019 over 2018