Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations

Given a large number of basis functions that can be potentially more than the number of samples, we consider the problem of learning a sparse target function that can be expressed as a linear combination of a small number of these basis functions. We are interested in two closely related themes: feature selection, or identifying the basis functions with nonzero coefficients;