On Learning from Ghost Imaging without Imaging

Computational ghost imaging is an imaging technique with which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for an ultrafast cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghost cytometry skipped the reconstruction of cell images from signals and directly used signals for cell-classification because this reconstruction is the bottleneck in a high-speed analysis. In this paper, we provide a theoretical analysis for learning from ghost imaging without imaging.