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High dimensional data clustering

Charles Bouveyron 1, 2 Stéphane Girard 2 Cordelia Schmid 1, * 
* Corresponding author
1 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 recurrent problem in many domains, for example in object recognition. High-dimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to high-dimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called High- Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization.
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  • HAL Id : inria-00548591, version 1



Charles Bouveyron, Stéphane Girard, Cordelia Schmid. High dimensional data clustering. 17th International Conference on Computational Statistics (Compstat '06), Aug 2006, Rome, Italy. pp.813--820. ⟨inria-00548591⟩



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