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Pré-Publication, Document De Travail Année : 2010

Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Résumé

Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult task from both the clustering accuracy and the result understanding points of view. This paper presents a discriminative latent mixture (DLM) model which models the data in a latent orthonormal discriminative subspace with an intrinsic dimension lower than the dimension of the original space. By constraining model parameters within and between groups, a family of 8 parsimonious DLM models is exhibited and this allows to fit onto various situations. An estimation algorithm, called the Fisher-EM algorithm, is also proposed for estimating both the mixture parameters and the discriminative subspace. Experiments on simulated and real datasets show that the proposed approach outperforms existing clustering methods and provides a useful representation of the clustered data. The method is as well applied to the clustering of mass spectrometry data.
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Dates et versions

hal-00492406 , version 1 (15-06-2010)
hal-00492406 , version 2 (28-09-2010)
hal-00492406 , version 3 (12-01-2011)
hal-00492406 , version 4 (19-04-2011)

Identifiants

  • HAL Id : hal-00492406 , version 1

Citer

Charles Bouveyron, Camille Brunet. Simultaneous model-based clustering and visualization in the Fisher discriminative subspace. 2010. ⟨hal-00492406v1⟩

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