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Conference papers
Clustering in Fisher Discriminative Subspaces
Abstract : Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult problem. This is mainly due to the fact that high-dimensional data usually live in low-dimensional subspaces hidden in the original space. This paper presents a model-based clustering approach which models the data in a discriminative subspace with an intrinsic dimension lower than the dimension of the original space. An estimation algorithm, called Fisher-EM algorithm, is proposed for estimating both the mixture parameters and the discriminative subspace. Experiments show that the proposed approach outperforms existing clustering methods and provides a useful representation of the high-dimensional data.
Cited literature [7 references]
https://hal-paris1.archives-ouvertes.fr/hal-00375581
Contributor : Camille Brunet <>
Submitted on : Wednesday, April 15, 2009 - 3:02:11 PM
Last modification on : Sunday, January 19, 2020 - 6:38:29 PM
Document(s) archivé(s) le : Thursday, June 10, 2010 - 8:33:09 PM
Contributor : Camille Brunet <>
Submitted on : Wednesday, April 15, 2009 - 3:02:11 PM
Last modification on : Sunday, January 19, 2020 - 6:38:29 PM
Document(s) archivé(s) le : Thursday, June 10, 2010 - 8:33:09 PM
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