HAL - INRIA :: [inria-00548517, version 1] Classification of high dimensional data: High Dimensional Discriminant Analysis

inria-00548517, version 1

Classification of high dimensional data: High Dimensional Discriminant Analysis

Charles Bouveyron () ^1, 2, Stephane Girard () ², Cordelia Schmid ( Author to contact preferably ) ¹

Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop (2005)

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.

1: LEAR (IMAG-INRIA Rhône-Alpes / GRAVIR)
CNRS : FR71 – CNRS : UMR5527 – INRIA – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
2: Laboratoire de Modélisation et Calcul (LMC - IMAG)
CNRS : UMR5523 – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)

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Submitted on: Monday, 20 December 2010 09:08:57
Updated on: Friday, 7 January 2011 15:57:13

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