Variable selection in model-based clustering and discriminant analysis with a regularization approach

Gilles Celeux (), Cathy Maugis-Rabusseau () and Mohammed Sedki ()
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Gilles Celeux: Inria and Université Paris-Sud
Cathy Maugis-Rabusseau: Université de Toulouse, INSA de Toulouse
Mohammed Sedki: Hôpital Paul Brousse

Advances in Data Analysis and Classification, 2019, vol. 13, issue 1, No 11, 259-278

Abstract: Abstract Several methods for variable selection have been proposed in model-based clustering and classification. These make use of backward or forward procedures to define the roles of the variables. Unfortunately, such stepwise procedures are slow and the resulting algorithms inefficient when analyzing large data sets with many variables. In this paper, we propose an alternative regularization approach for variable selection in model-based clustering and classification. In our approach the variables are first ranked using a lasso-like procedure in order to avoid slow stepwise algorithms. Thus, the variable selection methodology of Maugis et al. (Comput Stat Data Anal 53:3872–3882, 2000b) can be efficiently applied to high-dimensional data sets.

Keywords: Variable selection; Lasso; Gaussian mixture; Clustering; Classification; 62H30; 91C20 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-018-0322-5

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