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

Year: 2017

Type: preprint

Abstract: Relevant methods of variable selection have been proposed in model-based clustering and classification. These methods are making use of backward or forward procedures to define the roles of the variables. Unfortunately, these stepwise procedures are terribly slow and make these variable selection algorithms inefficient to treat large data sets. In this paper, an alternative regularization approach of variable selection is proposed for model-based clustering and classification. In this approach, the variables are first ranked with a lasso-like procedure in order to avoid painfully slow stepwise algorithms. Thus, the variable selection methodology of Maugis et al (2009b) can be efficiently applied on high-dimensional data sets.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1705.00946

Authors: Celeux, Gilles, Maugis-Rabusseau, Cathy, Sedki, Mohammed

Institutions: Université Paris-Sud, Institut de Mathématiques de Toulouse, Hôpital Paul-Brousse

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Institut national de recherche en informatique et en automatique (INRIA), Agence Nationale de la Recherche, Conseil Régional, Île-de-France, Fondation de Coopération Scientifique Campus Paris-Saclay

Award: Mixture-based procedures for statistical analysis of RNA-seq data

OpenAlex ID: W2950812147

Cites: 2

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