Computationally highly efficient mixture of adaptive filters

Simple item page
dc.citation.epage242en_US
dc.citation.issueNumber2en_US
dc.citation.spage235en_US
dc.citation.volumeNumber11en_US
dc.contributor.authorKilic, O. F.en_US
dc.contributor.authorSayin, M. O.en_US
dc.contributor.authorDelibalta, I.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T11:13:54Z
dc.date.available2018-04-12T11:13:54Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe 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.en_US
dc.identifier.doi10.1007/s11760-016-0925-2en_US
dc.identifier.issn1863-1703
dc.identifier.urihttp://hdl.handle.net/11693/37454
dc.language.isoEnglishen_US
dc.publisherSpringer Londonen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s11760-016-0925-2en_US
dc.source.titleSignal, Image and Video Processingen_US
dc.subjectAffine combinationen_US
dc.subjectBig dataen_US
dc.subjectComputational reductionen_US
dc.subjectConvex combinationen_US
dc.subjectMixture approachen_US
dc.subjectSet-membership filteringen_US
dc.titleComputationally highly efficient mixture of adaptive filtersen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Computationally highly efficient mixture of adaptive filters.pdf
Size:
594.43 KB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download

Collections

Department of Electrical and Electronics Engineering