Boosted adaptive filters

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buir.contributor.authorKari, Dariush
buir.contributor.authorMirza, Ali H.
buir.contributor.authorKozat, Süleyman Serdar
buir.contributor.authorKhan, Farhan
dc.citation.epage78en_US
dc.citation.spage61en_US
dc.citation.volumeNumber81en_US
dc.contributor.authorKari, Dariushen_US
dc.contributor.authorMirza, Ali H.en_US
dc.contributor.authorKhan, Farhanen_US
dc.contributor.authorÖzkan, H.en_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.date.accessioned2019-02-21T16:01:30Z
dc.date.available2019-02-21T16:01:30Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe introduce the boosting notion of machine learning to the adaptive signal processing literature. In our framework, we have several adaptive filtering algorithms, i.e., the weak learners, that run in parallel on a common task such as equalization, classification, regression or filtering. We specifically provide theoretical bounds for the performance improvement of our proposed algorithms over the conventional adaptive filtering methods under some widely used statistical assumptions. We demonstrate an intrinsic relationship, in terms of boosting, between the adaptive mixture-of-experts and data reuse algorithms. Additionally, we introduce a boosting algorithm based on random updates that is significantly faster than the conventional boosting methods and other variants of our proposed algorithms while achieving an enhanced performance gain. Hence, the random updates method is specifically applicable to the fast and high dimensional streaming data. Specifically, we investigate Recursive Least Square-based and Least Mean Square-based linear and piecewise-linear regression algorithms in a mixture-of-experts setting and provide several variants of these well-known adaptation methods. Furthermore, we provide theoretical bounds for the computational complexity of our proposed algorithms. We demonstrate substantial performance gains in terms of mean squared error over the base learners through an extensive set of benchmark real data sets and simulated examples.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:01:30Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipThis work is supported in part by Turkish Academy of Sciences Outstanding Researcher Programme, TUBITAK Contract No. 113E517 , and Turk Telekom Communications Services Incorporated . Appendix A
dc.embargo.release2020-10-01en_US
dc.identifier.doi10.1016/j.dsp.2018.07.012
dc.identifier.issn1051-2004
dc.identifier.urihttp://hdl.handle.net/11693/49861
dc.language.isoEnglish
dc.publisherElsevier
dc.relation.isversionofhttps://doi.org/10.1016/j.dsp.2018.07.012
dc.relation.project1.79769313486232E+308
dc.source.titleDigital Signal Processing: A Review Journalen_US
dc.subjectAdaptive filteringen_US
dc.subjectBoosted filteren_US
dc.subjectEnsemble learningen_US
dc.subjectMixture methodsen_US
dc.subjectOnline boostingen_US
dc.subjectSmooth boosten_US
dc.titleBoosted adaptive filtersen_US
dc.typeArticleen_US

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