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Variable selection in model-based clustering and discriminant analysis with a regularization approach. (English) Zbl 1474.62216

Summary: 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 C. Maugis et al. [Comput. Stat. Data Anal. 53, No. 11, 3872–3882 (2009; Zbl 1453.62154)] can be efficiently applied to high-dimensional data sets.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
91C20 Clustering in the social and behavioral sciences

Citations:

Zbl 1453.62154
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References:

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