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High-dimensional data clustering

Charles Bouveyron 1, 2, 3 Stéphane Girard 2, 3 Cordelia Schmid 4, * 
* Corresponding author
4 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
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 exist in different low-dimensional subspaces hidden in the original space. A family of Gaussian mixture models designed for high-dimensional data which combine the ideas of subspace clustering and parsimonious modeling are presented. 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. Experiments on artificial and real data sets show that HDDC outperforms existing methods for clustering high-dimensional data.
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Submitted on : Monday, December 20, 2010 - 9:49:07 AM
Last modification on : Thursday, January 20, 2022 - 5:30:13 PM


  • HAL Id : inria-00548573, version 1



Charles Bouveyron, Stéphane Girard, Cordelia Schmid. High-dimensional data clustering. [Research Report] 2006. ⟨inria-00548573⟩



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