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Latent Classes of Objects and Variable Selection

In: Compstat 2008

Author

Listed:
  • Giuliano Galimberti

    (University of Bologna, Statistics Department)

  • Angela Montanari

    (University of Bologna, Statistics Department)

  • Cinzia Viroli

    (University of Bologna, Statistics Department)

Abstract

In this paper we present a model based clustering approach which contextually performs dimension reduction and variable selection. In particular we assume that the data have been generated by a linear factor model with latent variables modeled as gaussian mixtures (thus obtaining dimension reduction) and we shrink the factor loadings, resorting to a penalized likelihood method, with an L1 penalty (thus realizing automatic variable selection). We derive an EM algorithm to obtain the penalized model estimates and a modified BIC criterion to select the penalization parameter. We evaluate the performance of the proposed method on simulated data.

Suggested Citation

  • Giuliano Galimberti & Angela Montanari & Cinzia Viroli, 2008. "Latent Classes of Objects and Variable Selection," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 373-383, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_31
    DOI: 10.1007/978-3-7908-2084-3_31
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