Compressive Mahalanobis classifiers

We propose a new framework for detection/estimation designed to avoid the loss of salient information in the process of reducing the dimensionality of digitized data. The main idea is a semi-supervised learning pre-processing scheme based on compressed sensing. The proposed approach combines a first step - performed at the data acquisition level - with an energy based algorithm aimed at defining a global metric on the data. The latter is then used to drive the classification algorithm. We demonstrate the power of the new technique by applying it to the detection of cellular nuclei in large, high-dimensional, hyperspectral images.