PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Class-Specific Subspace Discriminant Analysis for High-Dimensional Data
Charles Bouveyron, S. Girard and Cordelia Schmid
In: Subspace, Latent Structure and Feature Selection techniques Lecture Notes in Computer Science . (2006) Springer .


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 objects in real images and its performances are compared to classical classi cation methods.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1896
Deposited By:Charles Bouveyron
Deposited On:29 December 2005