In this paper we tackle the problem of recovering the phase of complex linear measurements whenonlymagnitude information isavailableandwecontrol the input. We are motivated by the recent development of dedicated optics-based hardware for rapid random projections which leverages the propagation of light inrandom media.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

In this paper,we consider the learning problem where the projected data isfurther compressed byscalarquantization, which iscalled quantized compressivelearning. Generalization error bounds are derived for three models: nearest neighbor (NN) classifier, linear classifier and least squares regression.

In the existing literature, a common approach to approximate the metric is to first find a map that preserves Euclidean lengths instead of distances.

Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the "sign flipping probability" of random projections.