HAL - INRIA :: [hal-00104065, version 1] Class-specific subspace discriminant analysis for high-dimensional data

hal-00104065, version 1

Class-specific subspace discriminant analysis for high-dimensional data

Charles Bouveyron () ^1, 2, Stéphane Girard () ¹, Cordelia Schmid () ²

Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005 Springer (Ed.) (2006) 139-150

Abstract: We propose a new method for discriminant analysis, called High Dimensional Discriminant Analysis (HDDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. We therefore propose a new parameterization of the Gaussian model to classify high-dimensional data. This parameterization takes into account the specific subspace and the intrinsic dimension of each class to limit the number of parameters to estimate. HDDA is applied to recognize object parts in real images and its performance is compared to classical methods.

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

hal-00104065, version 1
http://hal.archives-ouvertes.fr/hal-00104065
oai:hal.archives-ouvertes.fr:hal-00104065
From: Brigitte Bidegaray-Fesquet <>
Submitted on: Thursday, 5 October 2006 16:38:30
Updated on: Friday, 3 December 2010 10:49:29

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DOI: 10.1007/11752790_9

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