Computationally highly efficient mixture of adaptive filters |
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Authors: | O. Fatih Kilic M. Omer Sayin Ibrahim Delibalta Suleyman S. Kozat |
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Affiliation: | 1.The Department of Electrical and Electronics Engineering,Bilkent University,Bilkent, Ankara,Turkey;2.The Department of Electrical and Computer Engineering,University of Illinois at Urbana-Champaign,Champaign,USA;3.Turk Telekom Communications Services Inc.,Istanbul,Turkey |
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Abstract: | We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature. |
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