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Abstract

In this article we present a signal model for classification based on a collection of low dimensional subspaces embedded into the high dimensional signal space. We develop an alternate projection algorithm to find such a collection and finally test the classification performance of our scheme in comparison to Fisher's LDA and a recent approach based on sparse approximation.

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