High Dimensional Discriminant Analysis

Charles Bouveyron 1, 2 Stephane Girard 2 Cordelia Schmid 1, *
* Auteur correspondant
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
Abstract : We propose a new method of discriminant analysis, called High Di- mensional Discriminant Analysis (HHDA). Our approach is based on the assump- tion 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 object in real images and its performances are compared to classical classification methods.
Type de document :
Communication dans un congrès
International Conference on Applied Stochastic Models and Data Analysis, May 2005, Brest, France. pp.526--534, 2005, 〈http://conferences.telecom-bretagne.eu/asmda2005/article8fd3.html?id_article=37〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00548516
Contributeur : Thoth Team <>
Soumis le : lundi 20 décembre 2010 - 09:08:56
Dernière modification le : mercredi 11 avril 2018 - 01:54:44

Identifiants

  • HAL Id : inria-00548516, version 1

Collections

IMAG | INRIA | UGA

Citation

Charles Bouveyron, Stephane Girard, Cordelia Schmid. High Dimensional Discriminant Analysis. International Conference on Applied Stochastic Models and Data Analysis, May 2005, Brest, France. pp.526--534, 2005, 〈http://conferences.telecom-bretagne.eu/asmda2005/article8fd3.html?id_article=37〉. 〈inria-00548516〉

Partager

Métriques

Consultations de la notice

227