Browsing by Subject "Convex combination"

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Open Access
Computationally highly efficient mixture of adaptive filters
(Springer London, 2017) Kilic, O. F.; Sayin, M. O.; Delibalta, I.; Kozat, S. S.
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. © 2016, Springer-Verlag London.
Open Access
Mixture of set membership filters approach for big data signal processing
(IEEE, 2016) Kılıç, O. Fatih; Sayın, M. Ömer; Delibalta, İ.; Kozat, Süleyman Serdar
In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve significantly reduced computational load for the mixture methods with better performance in convergence rate and steady-state error with respect to conventional mixture methods. Finally, we approve these statements with the simulations done on produce data.