Constructing sparse KFDA using pre-image reconstruction

Kernel Fisher Discriminant Analysis (KFDA) improves greatly the classification accuracy of FDA via using kernel trick. However, the final solution of KFDA is expressed as an expansion of all training examples, which seriously undermines the classification efficiency, especially in real-time applications. This paper proposes a novel framework to construct sparse KFDA using pre-image reconstruction. The proposed method (PR-KFDA) appends greedily the pre-image of the residual between the current approximate model and the original KFDA model in feature space with the local optimal Fisher coefficients to acquire sparse representation of KFDA solution. Experimental results show that PR-KFDA can reduce the solution of KFDA effectively while maintaining comparable test accuracy.