PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

High Dimensional Discriminant Analysis
C. Bouveyron, S. Girard and C. Schmid
In: International Conference on Applied Stochastic Models and Data Analysis, Brest, France(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 di erent 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 classi cation methods.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1047
Deposited By:Charles Bouveyron
Deposited On:12 August 2005