A gradient based technique for generating sparse representation in function approximation

We provide an RKHS based inverse problem formulation[15] for analytically deriving the optimal function approximation when probabilistic information about the underlying regression is available in terms of the associated correlation functions as used in [9, 8]. On the lines of Poggio and Girosi[9], we show that this solution can be sparsified using principles of SVM and provide an implementation of this sparsification using a novel, conceptually simple and robust gradient based sequential method instead of the conventional quadratic programming routines.