We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.
['Tomaso Poggio', 'Federico Girosi']
A sparse representation for function approximation