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Book Sections Year : 2006

Class-specific subspace discriminant analysis for high-dimensional data

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.

Dates and versions

hal-00104065 , version 1 (05-10-2006)

Identifiers

Cite

Charles Bouveyron, Stéphane Girard, Cordelia Schmid. Class-specific subspace discriminant analysis for high-dimensional data. Craig Saunders; Marko Grobelnik; Steve Gunn; John Shawe-Taylor. Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Springer, pp.139-150, 2006, Lecture Notes in Computer Science volume 3940, ⟨10.1007/11752790_9⟩. ⟨hal-00104065⟩
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