Empirical quantization for sparse sampling systems

We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is based on empirical divergence maximization, an approach akin to the well-known technique of empirical risk minimization. We show that the estimator´s rate of convergence to the “best in class” estimate can be as fast as n^-1, where n equals the number of training samples.