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