eng URI http://hdl.handle.net/10795/2997 πληροφορική ανάλυση πληροφοριών δίκτυο πληροφορικής In this paper, a sparsity-promoting adaptive algorithm for distributed learning in diffusion networks is developed. The algorithm follows the set-theoretic estimation rationale, i.e., at each time instant and at each node, a closed convex set, namely a hyperslab, is constructed around the current measurement point. This defines the region in which the solution lies. The algorithm seeks a solution in the intersection of these hyperslabs by a sequence of projections. Sparsity is encouraged in two complimentary ways: a) by employing extra projections onto a weighted ℓ1 ball, that complies with our desire to constrain the respective weighted ℓ1 norm and b) by adopting variable metric projections onto the hyperslabs, which implicitly quantify data mismatch. A combine-adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys a number of elegant convergence properties. Finally, numerical examples verify the validity of the proposed scheme, compared to other algorithms, which have been developed in the context of sparse adaptive learning.compared to other algorithms, which have been developed in the context of sparse adaptive learning. 5 pp. LOMv1.0 creator LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2012 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Adaptive distributed learning Sparsity Diffusion networks Projections 349471 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2997/2/2997_1.69_%ce%94%ce%97%ce%9c_27_8_12.pdf URI http://hdl.handle.net/10795/2997 LOMv1.0creator 2016-05-04T12:14:12Z LOMv1.0validator 2016-05-04T12:14:12Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")