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Discriminative variable selection for clustering with the sparse Fisher-EM algorithm

Charles Bouveyron 1, Camille Brunet 2

(04/04/2012)

The interest in variable selection for clustering has increased recently due to the growing need in clustering high-dimensional data. Variable selection allows in particular to ease both the clustering and the interpretation of the results. Existing approaches have demonstrated the efficiency of variable selection for clustering but turn out to be either very time consuming or not sparse enough in high-dimensional spaces. This work proposes to perform a selection of the discriminative variables by introducing sparsity in the loading matrix of the Fisher-EM algorithm. This clustering method has been recently proposed for the simultaneous visualization and clustering of high-dimensional data. It is based on a latent mixture model which fits the data into a low-dimensional discriminative subspace. Three different approaches are proposed in this work to introduce sparsity in the orientation matrix of the discriminative subspace through \ell_{1} -type penalizations. Experimental comparisons with existing approaches on simulated and real-world data sets demonstrate the interest of the proposed methodology. An application to the segmentation of hyperspectral images of the planet Mars is also presented.

1 :	Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM)
	Université Paris I - Panthéon-Sorbonne
2 :	Modélisation aléatoire de Paris X (MODAL'X)
	Université Paris X - Paris Ouest Nanterre La Défense

Domaine

:

Mathématiques/Statistiques Statistiques/Théorie

hal-00685183, version 2

http://hal.archives-ouvertes.fr/hal-00685183

oai:hal.archives-ouvertes.fr:hal-00685183

Contributeur : Charles Bouveyron <>

Soumis le : Mardi 10 Avril 2012, 09:10:29

Dernière modification le : Mardi 10 Avril 2012, 11:30:11