eng URI http://hdl.handle.net/10795/2994 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. At each time instance and at each node of the network, a closed convex set, known as property set, is constructed based on the received measurements; this defines the region in which the solution is searched for. In this paper, the property sets take the form of hyperslabs. The goal is to find a point that belongs to the intersection of these hyperslabs. To this end, sparsity encouraging variable metric projections onto the hyperslabs have been adopted. In addition, sparsity is also imposed by employing variable metric projections onto weighted balls. A combine adapt cooperation strategy is adopted. Under some mild assumptions, the scheme enjoys monotonicity, asymptotic optimality and strong convergence to a point that lies in the consensus subspace. 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. 14 pp. LOMv1.0 creator LOMv1.0 publisher LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2012-06-15 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Adaptive distributed learning Diffusion networks Projections Sparsity 4375588 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2994/2/2994_1.69_%ce%94%ce%97%ce%9c_20_10_12.pdf URI http://hdl.handle.net/10795/2994 LOMv1.0creator 2016-05-04T11:50:53Z LOMv1.0validator 2016-05-04T11:50:53Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")