Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#235767] of Robert Calderbank

Papers Published

  1. Reboredo, H; Renna, F; Calderbank, R; Rodrigues, MRD, Projections designs for compressive classification, 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (December, 2013), pp. 1029-1032 [doi]
    (last updated on 2018/12/19)

    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.
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320