Approximation analysis of empirical feature-based learning with truncated sparsity

A sparse algorithm, based on empirical feature selection, is investigated from the viewpoint of learning theory. It is a novel way to realize sparse empirical feature-based learning different from the regularized kernel projection machines. Représenter theorem and error analysis of this algorithm are established without sparsity assumption of regression function. An empirical study verifies our theoretical analysis.