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

Abstract : 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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Preprints, Working Papers, ...
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Contributor : Charles Bouveyron Connect in order to contact the contributor
Submitted on : Tuesday, June 15, 2010 - 5:37:44 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Wednesday, September 15, 2010 - 8:41:12 PM


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  • HAL Id : hal-00492406, version 1



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



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