Classification of high dimensional data: High Dimensional Discriminant AnalysisReportar como inadecuado

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* Corresponding author 1 LEAR - Learning and recognition in vision GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71 2 LMC - IMAG - Laboratoire de Modélisation et Calcul

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.

Keywords : Discriminant analysis dimension reduction regularization

Autor: Charles Bouveyron - Stephane Girard - Cordelia Schmid -



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