Adaptive Mixture Methods Based on Bregman Divergences
This work provides incremental improvements in adaptive filtering methods for signal processing applications.
The paper tackles the problem of adaptively combining outputs from multiple filters to model a signal, using Bregman divergences to derive multiplicative updates for mixture weights, and demonstrates effectiveness for sparse systems with transient analysis.
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.