Projections designs for compressive classification

Published

Journal Article

This paper puts forth projections designs for compressive classification of Gaussian mixture models. In particular, we capitalize on the asymptotic characterization of the behavior of an (upper bound to the) misclassification probability associated with the optimal Maximum-A-Posteriori (MAP) classifier, which depends on quantities that are dual to the concepts of the diversity gain and coding gain in multi-antenna communications, to construct measurement designs that maximize the diversity-order of the measurement model. Numerical results demonstrate that the new measurement designs substantially outperform random measurements. Overall, the analysis and the designs cast geometrical insight about the mechanics of compressive classification problems. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Reboredo, H; Renna, F; Calderbank, R; Rodrigues, MRD

Published Date

  • December 1, 2013

Published In

  • 2013 Ieee Global Conference on Signal and Information Processing, Globalsip 2013 Proceedings

Start / End Page

  • 1029 - 1032

Digital Object Identifier (DOI)

  • 10.1109/GlobalSIP.2013.6737069

Citation Source

  • Scopus