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We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant Analysis (MPKFDA). We provide generalisation error bounds analogous to those constructed for the Robust Minimax algorithm together with a sample compression bounding technique. We present experimental results on real world datasets, which show that MP-KFDA is competitive with the KFDA and the SVM ondblp:journals/jmlr/DietheHHS09 fatcat:w2mtdoh5krazlpeoqdi3kxvip4