Online Learning with Kernels a New Approach for Sparsity Control Based on a Coherence Criterion

Kernel methods are well known standard tools for solving function approximation and pattern classification problems. In this paper, we consider online learning in a reproducing kernel Hilbert space. We develop a simple and computationally efficient algorithm for sparse solutions. The approach is based on sequential projection learning and the coherence criterion, which is a fundamental parameter to characterize dictionaries of functions in sparse approximation problems. Experimental results show the effectiveness of our approach.