eng URI http://hdl.handle.net/10795/2990 πληροφορική ανάλυση πληροφοριών δίκτυο πληροφορικής In this paper, a novel algorithm for bandwidth reduction in adaptive distributed learning is introduced. We deal with diffusion networks, in which the nodes cooperate with each other, by exchanging information, in order to estimate an unknown parameter vector of interest. We seek for solutions in the framework of set theoretic estimation. Moreover, in order to reduce the required bandwidth by the transmitted information, which is dictated by the dimension of the unknown vector, we choose to project and work in a lower dimension Krylov subspace. This provides the benefit of trading off dimensionality with accuracy. Full convergence properties are presented, and experiments, within the system identification task, demonstrate the robustness of the algorithmic technique. 4 pp. LOMv1.0 creator LOMv1.0 publisher LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2011-05 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Adaptive distributed learning, Krylov subspaces Projections 303090 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2990/2/2990_1.69_%ce%91%ce%9d_22_5_11.pdf URI http://hdl.handle.net/10795/2990 LOMv1.0creator 2016-05-04T09:40:33Z LOMv1.0validator 2016-05-04T09:40:33Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")