Classification via Incoherent Subspaces

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.