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In this paper, a novel scheme for online, sparsity-aware learning is presented. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may even be of a nonconvex nature. The complexity of the algorithm exhibits a linear dependence on the number of free parameters.doi:10.1109/icassp.2012.6288615 dblp:conf/icassp/KopsinisSTM12 fatcat:af3zb7khkjgsjiprda2k2owkhi