Projections Designs for Compressive Classification
; Calderbank, A. R. C.
; Rodrigues , M.
Projections Designs for Compressive Classification, Proc IEEE Global Conf. on Signal and Information Processing - Global SIP, Austin, United States, Vol. xx, pp. 1029 - 1032, December, 2013.
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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 the (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 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 offer considerable geometrical insight about the mechanics of compressive classification problems.