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Classification of high dimensional data: High Dimensional Discriminant Analysis

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 : We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in dierent subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt the Gaussian framework to high dimensional data. This regularization is achieved by assuming that classes are spherical in their eigenspace. HDDA is applied to recognize objects in real images and its performances are compared to classical classication methods.
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  • HAL Id : inria-00548517, version 1



Charles Bouveyron, Stéphane Girard, Cordelia Schmid. Classification of high dimensional data: High Dimensional Discriminant Analysis. Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop, Feb 2005, Bohinj, Slovenia. ⟨inria-00548517⟩



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