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Analyse Discriminante de Haute Dimension

Charles Bouveyron 1 Stéphane Girard 2 Cordelia Schmid 1
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 for discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different 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 natural images and its performances are compared to classical classification methods.
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Submitted on : Tuesday, May 23, 2006 - 2:51:11 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:36 PM
Long-term archiving on: : Sunday, April 4, 2010 - 10:03:48 PM

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Charles Bouveyron, Stéphane Girard, Cordelia Schmid. Analyse Discriminante de Haute Dimension. [Rapport de recherche] RR-5470, INRIA. 2005, pp.46. ⟨inria-00071243⟩

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