Adaptive Mixture Methods Based on Bregman Divergences

Details Adaptive Mixture Methods Based on Bregman Divergences Adaptive Mixture Methods Based on Bregman Divergences

2012-03-20

arxiv.org/abs/1203.4598v1

Computer Science

Learning

Submitted to Digital Signal Processing, Elsevier; IEEE.org

Scientific paper

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.

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