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

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.

• Publication year

  2017

• Venue

  Advances in Data Analysis and Classification

• Publication date

  2017-05-02

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/s11634-018-0322-5arXiv 1705.00946
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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