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      Adaptive mixture methods based on Bregman divergences

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      Author
      Donmez, M. A.
      Inan, H. A.
      Kozat, S. S.
      Date
      2013
      Source Title
      Digital Signal Processing: A Review Journal
      Print ISSN
      1051-2004
      Publisher
      Elsevier
      Volume
      23
      Issue
      1
      Pages
      86 - 97
      Language
      English
      Type
      Article
      Item Usage Stats
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      85
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      Abstract
      We investigate adaptive mixture methods that linearly combine outputs of m constituent filters running in parallel to model a desired signal. We use Bregman divergences and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of m constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.
      Keywords
      Adaptive mixture
      Affine mixture
      Bregman divergence
      Multiplicative update
      Affine Constraints
      Bregman divergences
      Desired signal
      Linear combinations
      Mean-square
      Mixture method
      Multiplicative updates
      Relative entropy
      Running-in
      Adaptive algorithms
      Entropy
      Mixtures
      Permalink
      http://hdl.handle.net/11693/21144
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.dsp.2012.09.006
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      • Department of Electrical and Electronics Engineering 3601
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