Bounds on the Number of Measurements for Reliable Compressive Classification

11 Jul 2016Hugo ReboredoFrancesco RennaRobert CalderbankMiguel R. D. Rodrigues

This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for random measurements and designed ones... (read more)

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