High-Dimensional Data Clustering

Charles Bouveyron 1, 2 Stephane Girard 2 Cordelia Schmid 3
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for high-dimensional data which combine the ideas of dimension reduction and parsimonious modeling. These models give rise to a clustering method based on the Expectation-Maximization algorithm which is called High-Dimensional Data Clustering (HDDC). In order to correctly fit the data, HDDC estimates the specific subspace and the intrinsic dimension of each group. Our experiments on artificial and real datasets show that HDDC outperforms existing methods for clustering high-dimensional data
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Article dans une revue
Computational Statistics and Data Analysis, Elsevier, 2007, 52 (1), pp.502-519. 〈10.1016/j.csda.2007.02.009〉
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Charles Bouveyron, Stephane Girard, Cordelia Schmid. High-Dimensional Data Clustering. Computational Statistics and Data Analysis, Elsevier, 2007, 52 (1), pp.502-519. 〈10.1016/j.csda.2007.02.009〉. 〈hal-00022183v4〉

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