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      Boosted LMS-based piecewise linear adaptive filters

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      Author
      Kari, Dariush
      Marivani, Iman
      Delibalta, İ.
      Kozat, Süleyman Serdar
      Date
      2016
      Source Title
      Proceedings of the 24th European Signal Processing Conference, EUSIPCO 2016
      Print ISSN
      2219-5491
      Publisher
      IEEE
      Pages
      1593 - 1597
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we have several adaptive constituent filters that run in parallel. For each newly received input vector and observation pair, each filter adapts itself based on the performance of the other adaptive filters in the mixture on this current data pair. These relative updates provide the boosting effect such that the filters in the mixture learn a different attribute of the data providing diversity. The outputs of these constituent filters are then combined using adaptive mixture approaches. We provide the computational complexity bounds for the boosted adaptive filters. The introduced methods demonstrate improvement in the performances of conventional adaptive filtering algorithms due to the boosting effect.
      Keywords
      Adaptive boosting
      Adaptive filtering
      Artificial intelligence
      Bandpass filters
      Learning systems
      Piecewise linear techniques
      Signal filtering and prediction
      Adaptive filtering algorithms
      Adaptive signal processing
      Boosting effects
      Input vector
      Machine learning applications
      Piecewise linear
      Adaptive filters
      Permalink
      http://hdl.handle.net/11693/37741
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/EUSIPCO.2016.7760517
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      • Department of Electrical and Electronics Engineering 3524
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