Generalized Bregman divergence and gradient of mutual information for vector Poisson channels

Published

Journal Article

We investigate connections between information-theoretic and estimation-theoretic quantities in vector Poisson channel models. In particular, we generalize the gradient of mutual information with respect to key system parameters from the scalar to the vector Poisson channel model. We also propose, as another contribution, a generalization of the classical Bregman divergence that offers a means to encapsulate under a unifying framework the gradient of mutual information results for scalar and vector Poisson and Gaussian channel models. The so-called generalized Bregman divergence is also shown to exhibit various properties akin to the properties of the classical version. The vector Poisson channel model is drawing considerable attention in view of its application in various domains: as an example, the availability of the gradient of mutual information can be used in conjunction with gradient descent methods to effect compressive-sensing projection designs in emerging X-ray and document classification applications. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Wang, L; Rodrigues, M; Carin, L

Published Date

  • December 19, 2013

Published In

Start / End Page

  • 454 - 458

International Standard Serial Number (ISSN)

  • 2157-8095

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2013.6620267

Citation Source

  • Scopus